International Journal of Photoenergy
Volume 2016 (2016), Article ID 8506193, 16 pages
http://dx.doi.org/10.1155/2016/8506193

Research Article

Solar Energy Validation for Strategic Investment Planning via Comparative Data Mining Methods: An Expanded Example within the Cities of Turkey

Oya H. Yuregir¹ and Cagri Sagiroglu²

¹Faculty of Engineering and Architecture, Industrial Engineering, Cukurova University, 01330 Adana, Turkey
²Faculty of Arts, IRIO Department, Groningen University, 9712 EK Groningen, Netherlands

Received 30 January 2016; Revised 18 April 2016; Accepted 4 May 2016

Academic Editor: Alessandro Burgio

Copyright © 2016 Oya H. Yuregir and Cagri Sagiroglu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Energy supply together with the data management is one of the key challenges of our century. Specifically, to decrease the climate change effects as energy requirement increases day by day poses a serious dilemma. It can be adequately reconciled with innovative data management in (renewable) energy technologies. The new environmental-friendly planning methods and investments that are discussed by researchers, governments, NGOs, and companies will give the basic and most important variables in shaping the future. We use modern data mining methods (SOM and -Means) and official governmental statistics for clustering cities according to their consumption similarities, the level of welfare, and growth rate and compare them with their potential of renewable resources with the help of Rapid Miner 5.1 and MATLAB software. The data mining was chosen to make the possible secret relations visible within the variables that can be unpredictable at first sight. Here, we aim to see the success level of the chosen algorithms in validation process simultaneously with the utilized software. Additionally, we aim to improve innovative approach for decision-makers and stakeholders about which renewable resource is the most suitable for an exact region by taking care of different variables at the same time.

1. Introduction

Today, concerns about the environment, energy security, and economic prosperity are increasingly developing. The International Energy Agency (2013-2014) estimated that demand for primary energy will increase globally by 55 percent between 2005 and 2030. Additionally, in developing countries where economies and population are expected to grow fast and primary energy demand is projected to grow by 74 percent during the same period, fossil fuels are expected to remain the dominant source of primary energy, accounting for 85 percent of the overall increase in global demand [1]. But the question is how long this unsustainable de facto situation can continue.

It is observable that since the 1990s, the attempt to convert production systems to clean production systems has not been the aim of the companies alone, but this was started to be implemented by joint operations between the industry, government bodies, and the public [2]. In addition to be a key criterion of the sustainable development stipulation, for business and states/interstates, the integration of social and ecological challenges by managing the data in an innovative manner into decision-making process from the beginning is of growing importance for minimizing future environmentally related risk and sustainable associated economic costs [3–5].

In this perspective, this research focuses on verification and experimental comparison of the two possible data mining options (and data mining itself) for new solar energy investment decisions. In Section 2, we gave the basic background in multivariate cluster analysis, including Self-Organizing Map and -Means with software tools for comparison. Additionally, we explained why these methods have been chosen. Our data owing to the cities have been gathered from governmental/special official statistics foundations via mail and/or face to face conversations. In Section 3, we commanded, compared, and discussed the graphics about solar radiation and consumption values which are created by Rapid Miner 5.1 and MATLAB. These programs have been used for comparison of the results, respectively. In Section 4, we summarized the results and we proposed our suggestions about algorithms in pattern recognition for validating the investment decision. As a result, in addition to present new approach for assessment of the (future) solar power investments, we will suggest the better algorithm and software for decision-making and optimization process for stakeholders.

2. Material and Methods

2.1. Data Mining and Clustering

The objective of data mining (DM) is to identify valid novel, potentially useful, and understandable correlations and patterns in existing data and finding useful patterns in data is known by different names in different communities [8, 9]. Chung and Gray and Liao and Triantaphyllou gathered the recent updates of data mining algorithms in different disciplines and diversified range of applications [10, 11]. Also from the ecology point of view, various successful studies were collected as examples of different applications [12]. While many data mining tasks follow a traditional, hypothesis-driven data analysis approach, it is commonplace to employ an opportunistic, data-driven approach that encourages the pattern detection algorithms to find useful trends, patterns, and relationships [6]. In this manner, as shown in Table 1, we decided the neural networks as the best solution for our study since it is important from the relationship point of view. Because of a modern analyzing studies complication and hardness to confirm (or validate) the results, it is better to use more than one method with the same variable attributes.

Table 1: Data analysis task and techniques [6].

Data analysis techniques
Data summarization
Segmentation
Classification
Prediction
Dependency analysis

Descriptive
and
visualization
•
•

•

Correlation
analysis

•

Cluster
analysis

•

Discriminant
analysis

•

Regression
analysis

•
•

Neural networks

Case-based reasoning

•

Decision
trees

•
•

Association rules

•

The similar trilemma of energy/data/optimization has been used to analyze clustering of 316 different European regions according to their cultural background [13]. From the other side, Seidler and Adderley used data mining approach to develop a proactive approach against the criminal networks [14]. Additionally, Santillana et al. [15] present a methodology mainly based on data mining and machine learning techniques not only for observation but also for estimation and evaluation of the influenza disease effects.

Clustering, as the main methodology of this study, is simply division of data into groups of similar objects [8]. There are so many methods and algorithms to do that in the literature. But, two different clustering algorithms are chosen to investigate the data of this study and compare their results: Self-Organizing Maps and -Means algorithms. Sobkowicz et al. (2012) [16], similar to us, used successfully these algorithms for modeling, simulating, and forecasting of the ideas in the web and social media. The general reasons for selecting these two algorithms are their popularity, flexibility, applicability, and handling high dimensionality [17].

Additionally, Figure 1 shows total electricity cost including construction, production, and decommissioning for renewal energy. Published in September 2014, the chart shows that electricity from hydro power is already more cost effective than electricity from other fuels. Even the price for electricity from wind power is becoming competitive in some cases with that of natural gas and almost with that of coal-generated energy. The price of power from solar panels is still high, but the cost differences decrease day-by-day mainly due to more efficient panels, mass production, and unstable fossil fuel costs. Many studies indicate that the transition cost of the energy system will be far less than the long term cost for keeping on using the fossil fuels. Since the most expensive energy is the still solar, the investment decision must be analyzed very carefully from various perspectives. In this study, we chose the solar energy values because it is the widest one and simple to build nearly all around the world. We used clustering for exposing the secret relationship among consumption similarities of cities for making cost effective investments. Therefore, it is expected to obtain new neighborly relations that will be the base of the new renewable source investments according to the new clustered structure.

Figure 1: Total electricity cost (source: International Renewable Energy Agency, REthinking Energy: Towards a new power system, 2014) [7].

2.2. Self-Organizing Maps

Self-Organizing Maps (SOM) were presented in the beginning of the 1980s by Kohonen, Finland academician. The main idea of SOM is to map the data patterns onto an -dimensional grid of units or neurons [18] and it is usually used for mapping high-dimensional data into one-, two-, or three-dimensional feature maps (simple geometric relationships) to increase intelligibility [19, 20]. Additionally, the SOM is an adaptive display method that is particularly suitable for the representation of structured/normalized statistical data. The mapping represents a data set in an ordered form, whereby mutual similarities of data samples will be visualized as geometric relations of the images of the samples on the map [21]. SOM procedures are used in a range of applications, but they are having a major impact in the fields of data exploration and data mining [22].

Traditional multivariate statistical approaches are often confused by data sets with variable relationships that are nonlinear, by data distributions that are abnormal (typically with multiple populations), and by the data sets themselves that may be disparate, sparsely filled (contain “nulls”) with both continuous and discontinuous numeric data and text [23]. The SOM, ordered vector-quantization approach can overcome many of these problematic issues [24] and due to its layer-based structure effects of independent variables that can be visible separately. Lately, Miche et al. [25] adapted also SOM clustering a priori knowledge successfully in 5 different examples.

2.3. K-Means Algorithm

The -Means algorithm, one of the mostly used and well-known clustering algorithms, is classified as a partition or nonhierarchical clustering method. -Means is typically used with the Euclidean metric for computing the distance between points and cluster centers [26]. It quickly converges to a local minimum of its cost function [27]. In -Means, because the centers are being connected to each other, the data in one- or two-dimension reflection in space can be achieved [28]. Additionally, -Means was successfully used as a solution for accuracy challenge in large-scale image classification as a part of hybrid model that shows the adaptable structure of the algorithm into different fields like another example [29] cited by Elssied et al. [30] from soft computing perspective.

Briefly, the primary objective of applying -Means is to provide groups whose members are close (have high similarity degree) and well separated. In the clustering process, there are no predefined classes and no examples that would show what kind of desirable relations should be valid among the data, so it is natural to be asked about the validity and quality of the results obtained [31]. At every step, the center of each cluster is recalculated by using the average vector of the objects (which are assigned to the same cluster) and every object can just be situated in one cluster [32].

2.4. System Architecture

As mentioned in many studies, focusing on just consumption values to analyze energy challenge is not enough by itself. Other necessary influencing factors should be cared for as well. In this manner, first we grouped the cities in accordance with their electricity consumption. Under consumption, we have seven different variables. At least, we compared 81 cities with seven different subtitles of total consumption (i.e., government, industry, agriculture, city lighting, housing, trade, and others) and we also took care of data between 2004 and 2014 (totally 5670 values). An example data set with the explanation is presented in Appendix A. Our iteration coefficient to reach the truer results is 1000 times. We used SOM and -Means (as a representative of the nonhierarchical methods) algorithms. Because we want to see the sensitiveness of the algorithms primarily with the limited data, we set the value of SOM and -Means equal and compared their performance when . (We picked 5 according to first observable outputs of randomly chosen data sets which were handled via MATLAB and Rapid Miner 5.1.)

Later, we normalized and clustered the data in SOM and then -Means with (first) both of the software programs: MATLAB and Rapid Miner. There are more than one data mining tool as R, WEKA, Orange, Rapid Miner, Tanagra, KNIME, and so forth in addition to MATLAB which is well known tool especially in engineering and mathematical disciplines. We chose Rapid Miner as an alternative to MATLAB mainly because it is user friendly and with modern structure in ingredients for free. Rapid Miner also has the most important connectors: databases, Excel, txt, html, CSV, and so forth. In Rapid Miner, it is easy to use wizards for setting up your data sources and a graphical environment for processing data flows. Rapid Miner can use every algorithm in the other programs and has very innovative pathways to offer possible solutions and mistakes. Although WEKA looks the most successful data mining tool according to the study of Borges et al. (2013) [33], they did not take Rapid Miner as a candidate in their research. Generally Rapid Miner, R, Weka, and KNIME have most of the desired characteristics for a fully functional platform [34], but lastly Piatetsky [35] presented that in 2013 Rapid Miner was the mostly used open source tool in real projects.

First of all, we manipulate our (prepared) data with -Means and SOM in MATLAB. However, we reduce the variables from seven to three to ensure especially duration and stability in MATLAB. Reduced data can be visualized in an easier way in two dimensions and it does not have meaningful effect on the results in case of using one of these methods: data aggregation, dimension reduction, data compression, and discretization [36]. During reducing variables, we calculated the most effective inputs (with standard variations) via principal component analysis method.

In this manner, Figure 2 shows the first clustering results on the graph briefly. The algorithm that has been used in MATLAB and the detailed results are also presented in Appendix B.

Figure 2: Clustering results.

Next, the same data were handled via Rapid Miner 5.1. in addition to MATLAB for comparison. The table in Appendix C shows the clustering results separately according to consumption values by -Means and SOM. From one perspective, it shows the relevance for investment requirements and the clusters.

After observing the clusters of similarities according to consumption (independent of geographical neighborhood), we decided to find similarities of their solar power potential. The detailed and sensitive data has been gathered from national and local meteorology authorities in addition to archives of Ministry of Energy. The summary of data and algorithm for MATLAB that has been utilized with the results are presented in Appendix D. Furthermore, the Rapid Miner 5.1. results for SOM and -Means are in Appendix E.

These two data sets (solar radiation and consumption) are suitable to compare for observing which algorithm is more accurate and sensitive. In this manner, Figure 3 illustrates clustering schema of the cities according to SOM. Based on the profiles of the clusters identified by the SOM, we could say that SOM grouped the clusters successfully but not too sensitively. The yellow part spreads out remarkable area. Dark yellow and orange parts are represented with just a small area. But still, five different groups can also be observed from the spreading of the colors which has already been presented with details in Appendix F.

Figure 3: The cities SOM neighbor weight distances (Rapid Miner 5.1).

Till now, we have analyzed which cities are similar to each other from the point of electricity consumption and potential solar power views and also which algorithm gives better results. But for better decision support system (to serve the other aim of the study) we should know which city/cluster will need more power in the future simultaneously. Accordingly, we must know at least the increase of population and the ratio of industrialization. For example, if a city has got its own rich solar power potential but always loses its population or goes back in the welfare indexes because of any reason, it “strongly” means that for this city extra investment will not be necessary.

Because -Means can provide more sensitive results versus SOM, from now on it has been decided to go on with -Means via Rapid Miner 5.1. In this context, Figure 4 shows the clustering results according to population forecasting. The cities are clustered according to their similarity in population growth ratio and the results are visualized by color codes. The data have been supplied from Turkish Statistical Institute and can be found in detail in Appendix F. As seen, in every cluster, it is possible to find a city from any geographical region. So, it is possible to find “sister cities/regions” by focusing on the reasons in further studies. Additionally, there have been always some exceptions like megacities (Istanbul, Ankara, and Izmir in our example) where their values are far from the rest of the data and they can create separate research areas to themselves.

Figure 4: Clustering results according to population forecasting.

Similarly, Figure 5 shows the clustering of cities with color codes (and the numbers on the graph) in different way according to their welfare indexes values. Their index values have been provided by Ministry of Development and could be found in Appendix G. The colors represent the cities and the dimensions symbolize the index value. Smaller bubbles show more developed and bigger bubbles represent less developed cities. It is easy to notice that there is really big difference between the two extremes.

Figure 5: Clustering cities according to their welfare indexes values.

Therefore, it is possible to compare the results of cities’ future energy demand indicators with their consumption-similarity neighborhood and their solar power neighborhood clusters. Decision-makers can see the basic decision support components in the integrity of a cluster.

3. Results and Discussion

As mentioned by World Bank and World Energy Council in 2015 Energy Trilemma Index [37, 38], energy-efficiency improvements have very positive developments in the field but they are still slow. Today’s geopolitical as well as geoeconomic reality is dominated by urbanized areas, mainly organized as cities. The sustainability of cities, in particular megalopolises of over 10 million inhabitants, depends on regional and global networks for their basic needs in food, water, and energy. Security of supply and quality of life strongly depend on the functioning of these networks. But the present technology for producing electricity from renewable resources is still more expensive than the conventional ones despite technological development and various governmental supports. So, it is important to take care of cities/regions renewable richness not only by itself but also from different ways which at least should include the population forecasting, industry, agriculture and transformation rate and possibilities, climate, and the consumption routines. For this aim, both -Means and SOM algorithms are suitable because of their convenience for huge and small data set, large and small number of clusters, and ideal and random data set usage and also feasible for different software alternatives. For nonoverlapping situations all the methods had good performance.

In our study, both algorithms were run with same data for similar number of iterations. Our result shows that -Means is more sensitive and easier to implement. Additionally, when the becomes greater, the performance of SOM decreases and when the data are not big enough, -Means creates more successful outputs. In this context our findings are similar with Mingoti and Lima (2006) [23] and Abbas (2008) [39]. Likewise, Shahapurkar and Sundareshan [40] presented the results of application of the -Means technique to data set which has 798 observations in 6 dimensions. According to their outcomes, -Means clustering algorithm can provide various advantages compared to the SOM in terms of point density, accuracy, topology preservation, and computational requirements. Also, de Castro Leão et al. [41] showed in their study that at last -Means can reach results as successful as SOM but in faster and simple manner which is partly similar with Bação et al. (2005) [42]. So, the paper shows that it is also possible to reach meaningful solutions without challenging huge amount of data via -Means in a simple way. Additionally, Rapid Miner reached more sensitive results against MATLAB about clustering and forecasting in this paper with the same data.

Besides, we showed that data mining methods can be used as a successful tool also in renewable energy investment planning as many other areas. This study also illustrated in an innovative way that data mining methods successfully exposed the hidden relationship between the requirements and assets simultaneously by covering welfare ratio and cities energy requirements forecasting accordance to their growth of population and development. For instance, if we focus on the clusters in Figures 4 and 5, we realized that the cities that are at the same cluster according to their population potential often show the same welfare indexes line. On the other side, for a specific city/region, one can find its solar energy power, future need, and its welfare situation at one glance as a whole. If we focus on one city where we need more electricity in the future and have rich solar potential, it does not mean that we should invest sun-based production facility for sure. (Generally, the renewable resources are more suitable for home-based usage than the industrial ones because of their investment costs and own restrictions.) Briefly, the intersection(s) of these can give glue for the (more) accurate and efficient investment decisions.

4. Conclusion

In this paper, we have compared two clustering algorithms, respectively, via two different software programs to see which one is more successful under certain circumstances with sufficient (limited) data for the usage in new renewable energy investment decisions. In order to compare both algorithms, we have utilized four different types of data: electricity consumption, solar power potential, population forecasting, and welfare indexes of the cities. Although both software programs and algorithms are adequate enough for the analysis, from the time, easiness, and workforce perspectives, -Means method and Rapid Miner tool have demonstrated their competence. This can help especially the social sciences researchers/decision-makers to check their theories in an easier way.

Additionally, we presented a validation system for administrators both in government and in private sector of energy management to keep track of the suitable areas of energy investment according to different factors such as region, population, and welfare. Particularly visualizing and analyzing the dynamics between the humanities and energy are innovative. This will offer new ways to improve insight in creating optimal strategies for sustainable smart energy systems. Thus, administrators can decide which city/cluster needs more energy and more importantly new energy investment. In this way, for future studies other factors affecting the energy consumption could be included for more specific results. This can be a start point of studies about nonconventional and not state centric but urban centric programming methods.

Appendix

A. Example Data Set (for 2004)

See Table 2.

Table 2: Example data for electricity consumption in sectors for 2004.

City
House
Commercial
Governmental
Industry
Agriculture
Street
lighting
Others

Adana
27,7
12,3
3,2
44,2
1,9
3,4
7,2

Adiyaman
21,6
6,4
3,0
52,3
6,6
1,0
9,1

Afyonkarahisar
23,9
11,8
3,2
35,3
12,0
3,4
10,4

Ağri
50,2
8,8
12,0
5,7
0,0
7,0
16,2

Amasya
35,6
9,2
3,6
38,3
4,8
7,0
1,6

Ankara
33,6
22,3
15,1
24,7
1,7
2,2
0,4

Antalya
28,4
38,6
9,5
10,4
2,8
3,2
7,2

Artvin
29,9
15,1
9,2
42,0
0,0
3,6
0,2

Aydin
36,9
19,4
4,2
28,3
4,9
1,1
5,3

Balikesir
30,4
14,0
4,2
34,1
1,6
2,6
13,1

Bilecik
8,8
4,9
1,6
81,4
0,3
1,2
1,9

Bingöl
51,1
10,9
16,9
2,1
0,1
9,2
9,7

Bitlis
40,6
10,9
8,1
5,1
3,9
14,3
17,0

Bolu
16,6
17,0
3,6
51,5
0,3
3,1
7,8

Burdur
12,2
6,4
2,0
73,2
3,3
2,0
1,0

Bursa
17,3
10,5
1,8
65,5
0,6
1,0
3,3

Çanakkale
10,8
5,3
1,8
76,1
1,0
1,5
3,4

Çankiri
32,0
12,0
9,3
38,0
0,8
6,5
1,3

Çorum
30,7
11,6
3,2
41,7
2,2
4,3
6,3

Denizli
18,9
10,5
2,5
59,0
2,2
1,3
5,5

Diyarbakir
36,2
12,8
13,7
14,3
6,6
2,5
13,9

Edirne
26,1
12,5
4,2
34,0
10,4
2,8
10,0

Elaziğ
25,9
7,4
28,1
23,4
7,5
2,4
5,3

Erzincan
40,5
14,7
10,2
8,0
5,5
5,7
15,2

Erzurum
38,6
13,7
18,4
19,2
0,5
6,7
2,9

Eskişehir
21,9
10,0
6,3
47,0
5,0
2,8
6,9

Gaziantep
17,5
8,8
1,4
61,3
2,3
2,9
5,8

Giresun
51,3
13,3
5,8
14,9
0,0
9,5
5,1

Gümüşhane
51,0
14,6
9,1
5,2
1,5
10,3
8,3

Hakkari
33,5
7,3
22,9
0,4
0,0
30,8
5,1

Hatay
13,7
5,3
2,1
70,7
4,1
1,4
2,7

Isparta
19,4
3,8
7,3
61,0
4,8
2,0
1,7

Mersin
30,8
14,0
6,4
36,4
2,9
3,9
5,5

Istanbul
34,3
28,4
3,2
29,0
0,0
1,5
3,5

Izmir
20,1
9,9
2,7
58,9
2,1
2,4
3,9

Kars
27,1
9,9
9,3
22,7
0,1
10,5
20,4

Kastamonu
32,0
12,9
6,9
43,0
0,6
3,6
1,0

Kayseri
22,4
10,5
1,3
54,1
3,2
2,8
5,7

Kirklareli
12,1
5,0
1,5
74,1
0,1
0,9
6,2

Kirşehir
29,2
10,0
10,1
39,4
4,2
5,9
1,1

Kocaeli
8,6
6,6
1,8
81,7
0,0
0,7
0,6

Konya
17,4
8,8
4,5
47,8
16,9
2,5
2,1

Kütahya
24,0
10,9
3,6
52,9
0,3
3,5
4,7

Malatya
27,4
12,8
7,6
36,6
6,9
5,9
2,8

Manisa
23,7
8,7
3,1
45,6
9,2
2,7
7,0

K. maraş
13,5
5,2
1,5
69,8
2,2
1,8
6,0

Mardin
14,3
5,5
7,6
27,8
23,0
5,4
16,4

Muğla
30,7
32,7
5,2
8,9
1,4
2,1
19,0

Muş
38,3
11,0
24,2
5,5
0,2
14,8
5,9

Nevşehir
21,7
13,3
8,2
21,3
28,5
5,8
1,3

Niğde
15,1
5,2
7,5
45,1
23,9
2,2
1,1

Ordu
39,3
11,4
4,2
33,0
0,0
8,4
3,6

Rize
34,8
13,1
5,0
39,7
0,0
4,8
2,6

Sakarya
23,6
14,6
3,2
47,8
0,3
2,9
7,6

Samsun
32,6
11,7
5,0
41,0
2,3
3,9
3,5

Siirt
21,9
7,0
10,0
50,7
0,5
6,9
3,0

Sinop
45,9
14,7
6,1
20,3
1,1
8,3
3,7

Sivas
24,3
8,5
5,8
49,7
1,8
5,7
4,2

Tekirdağ
10,0
4,0
0,9
78,6
0,1
0,8
5,7

Tokat
37,3
11,9
5,0
30,0
1,7
6,0
8,1

Trabzon
44,2
16,9
5,4
18,9
0,1
6,1
8,4

Tunceli
38,4
9,9
27,1
4,7
0,1
9,7
10,0

Ş. urfa
30,2
8,9
3,4
17,8
23,0
2,8
13,8

Uşak
17,5
9,4
1,6
62,6
1,4
2,0
5,5

Van
41,0
16,9
9,4
12,4
0,1
6,6
13,6

Yozgat
29,3
10,6
3,5
28,9
11,0
7,2
9,5

Zonguldak
13,7
3,6
2,9
76,2
0,0
2,2
1,4

Aksaray
26,1
10,5
6,0
21,8
29,7
5,0
0,8

Bayburt
52,7
13,8
7,8
11,9
0,9
8,7
4,2

Karaman
18,8
7,0
5,4
33,1
30,8
2,9
2,0

Kirikkale
25,5
9,2
4,1
49,8
0,7
4,1
6,5

Batman
28,6
11,0
5,3
33,0
2,5
8,6
11,0

Şirnak
37,6
9,7
20,4
3,4
2,1
0,7
26,0

Bartin
41,2
13,7
13,5
26,6
0,1
5,0
0,0

Ardahan
50,7
17,4
16,0
0,4
0,0
9,2
6,3

Iğdir
47,8
14,2
18,4
6,7
0,0
10,4
2,5

Yalova
20,4
11,4
5,1
60,0
0,3
0,4
2,2

Karabük
19,6
5,5
2,4
69,3
0,1
2,3
0,7

Kilis
41,0
9,8
10,2
16,7
7,4
5,6
9,3

Osmaniye
27,7
10,3
6,2
47,3
2,0
1,8
4,7

Düzce
26,1
11,5
6,5
47,0
0,0
3,8
5,2

    Total
24,4
14,8
4,5
46,2
2,9
2,5
4,7

As an example data set for one year, our data goes back for ten years for 81 cities. The subtitles under consumption include the following:

(i) House: the electricity usage in ordinary homes by ordinary citizens.

(ii) Commercial: the consumption in commercial buildings like supermarkets, small enterprises, trade centers, and so forth.

(iii) Governmental: the consumption in all governmental buildings and facilities like ministries, schools, courts, public parks and universities, and so forth.

(iv) Industry: energy consumption by industry representing the usage in industrial buildings as factories and any kind of production facilities.

(v) Agriculture: the consumption especially in fields or greenhouse facilities for irrigation, spraying, pruning or planting, and so forth.

(vi) Street lighting: the electricity consumption for lighting the streets and roads during nights.

(vii) Others: the amount of illegal usage of electricity.

B. Clustering Algorithm for MATLAB and Results

The Algorithm

 [D,txt] = xlsread('inputs.xls'); %idx_som will give the group indices of each element for som clustering [idx_som s] = som_clust(D, 'maxclust', 5, ); idx_som %IDX_kmeans will contain the group numbers of cities %group centers will contain the group averages [IDX_kmeans, group_centers] = KMEANS(D', 5); figure;bar(hist(IDX_kmeans));title('-meansclustering histogram'); IDX_kmeans Cities_list = txt(3:end,1); xlswrite('output_som.xls'cities_list, 81, 'A1:A81'); xlswrite('output_som.xls', idx_som, 81, 'B1:B81'); xlswrite('output_kmeans.xls', cities_list, 81, 'A1:A81'); xlswrite('output_kmeans.xls', IDX_kmeans, 81, 'B1:B81');

Results

Cluster 1 Cities. “İstanbul”

Cluster 2 Cities. “Adana”, “Denizli”, “Gaziantep”, “Hatay”, “Kayseri”, “Konya”, “Manisa”, “Mersin”, “Tekirdağ”, “Zonguldak”, “Çanakkale”.

Cluster 3 Cities. “Bursa”, “Kocaeli”, “İzmir”

Cluster 4 Cities. “Adıyaman”, “Aksaray”, “Amasya”, “Artvin”, “Aydın”, “Balıkesir”, “Batman”, “Bayburt”, “Bolu”, “Burdur”, “Bilecik”, “Bingöl”, “Bitlis”, “Düzce”, “Diyarbakır”, “Edirne”, “Elazığ”, “Erzurum”, “Erzincan”, “Eskişehir”, “Gümüşhane”, “Giresun”, “Isparta”, “Iğdır”, “Karabük”, “Karaman”, “Kars”, “Kastamonu”, “Kırıkkale”, “Kırklareli”, “Kırşehir”, “Kütahya”, “Kilis”, “Malatya”, “Mardin”, “Muğla”, “Muş”, “Nevşehir”, “Niğde”, “Osmaniye”, “Sakarya”, “Samsun”, “Sinop”, “Sivas”, “Siirt”, “Tokat”, “Trabzon”, “Tunceli”, “Uşak”, “Van”, “Yalova”, “Yozgat”, “Çankırı”, “Çorum”, “Şırnak”

Cluster 5 Cities. “Ankara”, “Antalya”

C. Clustering via Rapid Miner and Results

See Table 3.

Table 3: Clustering of cities according to their consumption levels via Rapid Miner (by -Means and SOM).

Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM

Adana
5
5
Corum
3
5
Kayseri
5
5
Sinop
1
4
Burdur
3
5

Adıyaman
5
5
Denizli
3
5
Kilis
3
5
Sırnak
5
5
Bursa
3
5

A. karahisar
3
5
Diyarbakir
5
5
Kırıkkkale
3
5
Sivas
3
5
Canakkale
5
5

Agrı
3
5
Duzce
3
4
Kırklareli
2
4
Tekirdag
2
5
Cankiri
3
1

Aksaray
5
5
Edirne
5
4
Kırsehir
5
5
Tokat
1
5
Karabuk
3
5

Amasya
1
5
Elazıg
5
5
Kocaeli
3
5
Trabzon
4
3
Karaman
3
5

Ankara
3
5
Erzincan
3
5
Konya
5
5
Tunceli
5
5
Kars
1
5

Antalya
1
5
Erzurum
1
5
Kutahya
3
5
Usak
3
5
Kastamonu
3
5

Ardahan
4
3
Eskisehir
3
5
Malatya
5
5
Van
1
2
Kars
1
5

Artvin
4
1
Gaziantep
3
5
Manisa
3
5
Yalova
3
5
Rize
4
2

Aydın
3
5
Giresun
1
2
Mardin
2
5
Yozgat
3
5
K. maras
5
5

Balıkesir
3
5
Gumushane
1
5
Mersin
1
5
Zonguldak
3
2
Bolu
3
5

Bartın
3
5
Hakkari
1
5
Mugla
3
5
Sakarya
3
5

Batman
2
5
Hatay
1
5
Mus
5
5
Samsun
1
4

Bayburt
1
5
Iğdır
1
5
Nevsehir
5
5
Sanlıurfa
5
5

Bilecik
3
1
Isparta
3
5
Nigde
5
5
Siirt
5
5

Bingol
5
5
İstanbul
5
5
Ordu
1
5
Karabuk
3
5

Bitlis
5
1
İzmir
3
5
Osmaniye
3
5
Karaman
3
5

D. Clustering Results according to Solar Power Potential with Algorithm for MATLAB

See Table 4.

 [num,txt,raw] = xlsread('SSAPMA.xls','SSAPMAS'); cities = txt(3:end,1); [idx,c] = kmeans(num,5); for kume = 1:5 disp('=================='),disp(['Category ' num2str(cluster)]) disp('==================') disp(sehirler(idx==cluster)) disp(['Center of cluster = ' num2str(c(cluster))]) end

Table 4

City
Sta. deviation
(radiation time)
City
Sta. deviation
(radiation time)

Adana
2,690296818
Kars
2,376578277

Adıyaman
2,850568535
Kastamonu
2,505402357

Afyonkarahısar
2,652801847
Kayserı
2,819897555

Agrı
2,531419233
Kılıs
2,441220522

Aksaray
2,793338678
Kırıkkale
2,590015551

Amasya
2,348400165
Kırklarelı
3,134691527

Ankara
2,575261583
Kırsehır
2,712404632

Antalya
2,362896038
Kocaelı
2,569262081

Ardahan
1,898816883
Konya
2,710960649

Artvın
1,667970115
Kutahya
2,54169071

Aydın
2,575892914
Malatya
2,835966791

Balıkesır
2,573129849
Manısa
2,631594758

Bartın
2,600760973
Mardın
2,976130971

Batman
2,902839127
Mersın
2,352446717

Bayburt
2,221639785
Mugla
2,474877522

Bılecık
2,559909938
Mus
2,781276186

Bıngol
2,74762966
Nevsehır
2,80581935

Bıtlıs
2,790823753
Nıgde
2,761498562

Bolu
2,513181361
Ordu
2,099196109

Burdur
2,562343762
Osmanıye
2,601296018

Bursa
2,56136506
Rıze
1,754007317

Canakkale
2,746524572
Sakarya
2,537168561

Cankırı
2,464731223
Samsun
2,156772763

Corum
2,476598249
Sanlıurfa
2,792549114

Denızlı
2,553234332
Sıırt
2,811756017

Dıyarbakır
2,883200864
Sınop
2,30354437

Duzce
2,539263208
Sırnak
2,823885719

Edırne
2,797553867
Sıvas
2,625525344

Elazıg
2,864982548
Tekırdag
2,950707425

Erzıncan
2,534056229
Tokat
2,370434061

Erzurum
2,375284194
Trabzon
1,848153733

Eskısehır
2,663166974
Tuncelı
2,79146219

Gazıantep
2,567747067
Usak
2,522644115

Gıresun
2,102217579
Van
2,347192768

Gumushane
2,256866682
Yalova
2,639856372

Hakkarı
2,140370392
Yozgat
2,673903072

Hatay
2,163409571
Zonguldak
2,56165135

Igdır
2,342966276
Kahramanmaras
2,790663377

Isparta
2,615078074
Karabuk
2,505135143

Istanbul
2,785876458
Karaman
2,639941471

Izmır
2,674965213

Results

Category 1 (center of cluster = 2.54 (the median point of the values in the same cluster)). “Agri”, “Ankara”, “Aydin”, “Balikesir”, “Bartin”, “Bilecik”, “Bolu”, “Burdur”, “Bursa”, “Cankiri”, “Corum” “Denizli”, “Duzce”, “Erzincan”, “Gaziantep”, “Karabuk”, “Kastamonu”, “Kilis”, “Kirikkale”, “Kocaeli”, “Kutahya”, “Mugla”, “Osmaniye”, “Sakarya”, “Usak”, “Zonguldak”

Category 2 (center of cluster = 1.7922). “Ardahan”, “Artvin”, “Rize”, “Trabzon”

Category 3 (center of cluster = 2.6791). “Adana”, “Afyonkarahisar”, “Bingol”, “Canakkale”, “Eskisehir”, “Isparta”, “Izmir”, “Karaman”, “Kirsehir” “Konya”, “Manisa”, “Nigde”, “Sivas”, “Yalova”, “Yozgat”

Category 4 (center of cluster= 2.8492). “Adiyaman”, “Aksaray”, “Batman”, “Bitlis”, “Diyarbakir”, “Edirne”, “Elazig”, “Istanbul”, “Kahramanmaras”, “Kayseri”, “Kirklareli”, “Malatya”, “Mardin”, “Mus”, “Nevsehir”, “Sanliurfa” “Siirt”, “Sirnak”, “Tekirdag” “Tunceli”

Category 5 (center of cluster = 2.27). “Amasya”, “Antalya”, “Bayburt”, “Erzurum”, “Giresun”, “Gumushane”, “Hakkari”, “Hatay”, “Igdir”, “Kars”, “Mersin”, “Ordu”, “Samsun”, “Sinop”, “Tokat”, “Van”

E. Clustering Results according to Solar Power Potential with Rapid Miner (by -means and SOM)

See Table 5.

Table 5: Clustering of cities according to their solar power potential via Rapid Miner (by -Means and SOM).

Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM
Cities
-Means
SOM

Adana
3
5
Corum
1
5
Kayseri
4
5
Sinop
5
5
Burdur
1
5

Adıyaman
4
5
Denizli
1
5
Kilis
1
5
Sırnak
4
5
Bursa
1
5

A. karahisar
3
5
Diyarbakir
4
5
Kırıkkkale
1
5
Sivas
3
5
Canakkale
3
5

Agrı
1
5
Duzce
1
5
Kırklareli
4
4
Tekirdag
4
5
Cankiri
1
5

Aksaray
4
4
Edirne
4
5
Kırsehir
3
5
Tokat
5
5
Karabuk
1
5

Amasya
5
5
Elazıg
4
5
Kocaeli
1
5
Trabzon
2
3
Karaman
3
5

Ankara
1
5
Erzincan
1
5
Konya
3
5
Tunceli
4
5
Kars
5
5

Antalya
5
5
Erzurum
5
5
Kutahya
1
5
Usak
1
5
Kastamonu
1
5

Ardahan
2
3
Eskisehir
3
5
Malatya
4
5
Van
5
5
Kars
5
5

Artvin
2
1
Gaziantep
1
5
Manisa
3
5
Yalova
3
5
Rize
2
2

Aydın
1
5
Giresun
5
5
Mardin
4
5
Yozgat
3
5
K. maras
4
5

Balıkesir
1
5
Gumushane
5
5
Mersin
5
5
Zonguldak
1
2
Bolu
1
5

Bartın
1
5
Hakkari
5
5
Mugla
1
5
Sakarya
1
5

Batman
4
4
Hatay
5
5
Mus
4
5
Samsun
5
5

Bayburt
5
5
Iğdır
5
5
Nevsehir
4
5
Sanlıurfa
4
5

Bilecik
1
5
Isparta
3
5
Nigde
3
5
Siirt
4
5

Bingol
3
5
İstanbul
4
5
Ordu
5
5
Karabuk
1
5

Bitlis
4
5
İzmir
3
5
Osmaniye
1
5
Karaman
3
5

F. Population and Population Forecasting of the Cities

See Table 6.

Table 6: Population and population forecasting.

City
2010
2013
2018
City
2010
2013
2018

Adana
2.053.465
2.088.667
2.132.063
Manisa
1.342.281
1.379.025
1.435.691

Adıyaman
586.015
585.186
576.566
K. Maraş
1.070.647
1.131.705
1.230.314

Afyon
699.435
699.222
690.944
Mardin
761.087
775.180
792.768

Ağrı
529.263
522.044
502.477
Muğla
824.009
872.597
952.629

Amasya
321.808
317.933
308.935
Muş
386.845
358.135
303.076

Ankara
4.686.009
4.885.314
5.200.437
Nevşehir
284.083
287.204
291.079

Antalya
1.960.779
2.112.823
2.364.692
Niğde
350.013
366.502
391.436

Artvin
162.654
156.276
144.708
Ordu
721.843
723.376
720.102

Aydın
995.256
1.039.498
1.111.880
Rize
320.057
320.372
319.334

Balıkesir
1.151.826
1.182.196
1.227.914
Sakarya
874.881
908.937
962.535

Bilecik
193.516
193.554
192.431
Samsun
1.243.063
1.254.323
1.266.147

Bingöl
262.113
271.191
285.864
Siirt
317.155
344.970
395.875

Bitlis
323.902
317.820
303.224
Sinop
202.463
204.724
207.404

Bolu
268.277
266.087
259.305
Sivas
620.350
599.974
555.460

Burdur
246.624
244.404
238.345
Tekirdağ
834.868
932.255
1.097.284

Bursa
2.629.919
2.813.935
3.122.382
Tokat
619.250
619.802
613.781

Çanakkale
480.358
488.070
499.377
Trabzon
751.839
754.895
756.547

Çankırı
178.941
182.606
187.140
Tunceli
87.764
89.706
92.700

Çorum
536.874
521.868
491.257
Şanlıurfa
1.630.731
1.716.085
1.856.962

Denizli
934.733
958.709
994.970
Uşak
334.720
334.453
331.276

Diyarbakır
1.531.760
1.587.863
1.670.933
Van
1.051.872
1.125.180
1.250.414

Edirne
393.537
391.225
385.850
Yozgat
464.120
430.652
366.183

Elazığ
548.206
547.831
543.067
Zonguldak
620.069
620.198
616.773

Erzincan
210.123
208.845
205.292
Aksaray
378.316
388.990
404.248

Erzurum
744.843
694.706
597.331
Bayburt
72.809
68.106
59.193

Eskişehir
768.363
807.893
871.942
Karaman
236.899
246.889
263.218

Gaziantep
1.673.920
1.763.230
1.900.432
Kırıkkale
272.884
261.793
239.463

Giresun
425.732
430.608
436.138
Batman
506.690
539.309
595.643

Gümüşhane
133.805
137.039
141.200
Şırnak
458.158
504.989
591.400

Hakkâri
280.518
314.949
375.488
Bartın
190.357
197.658
209.303

Hatay
1.449.059
1.500.126
1.578.655
Ardahan
107.690
100.441
87.273

Isparta
412.916
419.760
427.768
Iğdır
182.881
180.291
173.562

Mersin
1.626.691
1.659.526
1.705.843
Yalova
220.737
256.136
315.925

İstanbul
13.050.933
13.571.135
14.407.233
Karabük
216.389
216.172
214.829

İzmir
3.900.770
4.055.605
4.304.649
Kilis
124.843
130.407
138.891

Kars
302.562
286.295
253.943
Osmaniye
480.250
518.737
541.319

Kastamonu
361.138
361.121
358.235
Düzce
337.201
349.642
369.165

Kayseri
1.217.110
1.264.676
1.339.823
Kütahya
556.534
539.844
505.554

Kırklareli
337.576
338.173
338.349
Malatya
739.902
748.731
761.343

Kırşehir
220.861
217.098
208.230

Kocaeli
1.573.424
1.699.004
1.908.939

Konya
2.004.743
2.050.921
2.111.845

G. Welfare Indexes

See Table 7.

Table 7: Welfare indexes.

Rank
⁢First term (1990–1994)
Rank
⁢Second term (1995–2010)

City
Index value
Rank
City
Index value
City
Index value
Rank
City
Index value

1
İstanbul
4,270
41
Artvin
−,278
1
İstanbul
4,465
41
Kütahya
−,079

2
İzmir
2,689
42
Afyon
−,309
2
İzmir
2,549
42
Kastamonu
−,115

3
Ankara
2,441
43
Kastamonu
−,310
3
Ankara
2,477
43
Karaman
−,117

4
Bursa
1,577
44
Giresun
−,327
4
Bursa
1,651
44
Malatya
−,150

5
Kocaeli
1,574
45
Çorum
−,358
5
Kocaeli
1,355
45
Giresun
−,172

6
Adana
1,088
46
Sivas
−,434
6
Eskişehir
1,163
46
Niğde
−,177

7
Tekirdağ
1,070
47
Ordu
−,441
7
Adana
1,127
47
Afyon
−,183

8
Antalya
1,028
48
Niğde
−,450
8
Tekirdağ
1,077
48
Çorum
−,242

9
Eskişehir
,969
49
Aksaray
−,468
9
Antalya
1,068
49
K. Maraş
−,274

10
Balıkesir
,901
50
K. Maraş
−,469
10
Muğla
,915
50
Osmaniye
−,308

11
İçel
,863
51
Erzincan
−,473
11
Balıkesir
,912
51
Bartın
−,334

12
Aydın
,784
52
Çankırı
−,506
12
Kırklareli
,760
52
Sivas
−,396

13
Muğla
,742
53
Diyarbakır
−,532
13
Edirne
,721
53
Sinop
−,397

14
Kırklareli
,660
54
Sinop
−,540
14
Denizli
,719
54
Aksaray
−,402

15
Çanakkale
,606
55
Tokat
−,547
15
Çanakkale
,717
55
Çankırı
−,427

16
Konya
,590
56
Yozgat
−,656
16
Konya
,712
56
Tokat
−,439

17
Denizli
,561
57
Erzurum
−,679
17
Aydın
,644
57
Tunceli
−,439

18
Manisa
,559
58
Şanlıurfa
−,753
18
İçel
,599
58
Erzincan
−,468

19
Kayseri
,553
59
Adıyaman
−,849
19
Yalova
,580
59
Ordu
−,471

20
Edirne
,492
60
Tunceli
−,859
20
Manisa
,518
60
Yozgat
−,519

21
Bilecil
,404
61
Gümüşhane
−,904
21
Zonguldak
,483
61
Erzurum
−,639

22
Isparta
,394
62
Bayburt
−1,023
22
Kayseri
,397
62
Gümüşhane
−,709

23
Zonguldak
,338
63
Batman
−1,048
23
Hatay
,389
63
Diyarbakır
−,743

24
Sakarya
,337
64
Kars
−1,062
24
Sakarya
,371
64
Kilis
−,748

25
Hatay
,328
65
Siirt
−1,084
25
Bilecik
,368
65
Şanlıurfa
−,834

26
Bolu
,198
66
Mardin
−1,094
26
Bolu
,347
66
Adıyaman
−,919

27
Kırıkkale
,190
67
Van
−1,179
27
Samsun
,342
67
Bayburt
−1,017

28
Burdur
,168
68
Hakkâri
−1,242
28
Isparta
,329
68
Kars
−1,083

29
Samsun
,139
69
Bingöl
−1,293
29
Burdur
,295
69
Iğdır
−1,093

30
Uşak
,108
70
Bitlis
−1,340
30
Kırıkkale
,172
70
Ardahan
−1,157

31
Elazığ
,057
71
Şırnak
−1,356
31
Uşak
,159
71
Mardin
−1,163

32
Trabzon
−,004
72
Ağrı
−1,553
32
Trabzon
,142
72
Batman
−1,166

33
Nevşehir
−,006
73
Muş
−1,566
33
Rize
,111
73
Bingöl
−1,230

34
Rize
−,051

34
Karabük
,110
74
Siirt
−1,245

35
Kütahya
−,058

35
Nevşehir
,078
75
Van
−1,312

36
Kırşehir
−,096

36
Amasya
,038
76
Bitlis
−1,439

37
Malatya
−,104

37
Artvin
,013
77
Muş
−1,483

38
Gaziantep
−,118

38
Elazığ
,011
78
Şırnak
−1,542

39
Amasya
−,119

39
Gaziantep
,006
79
Ağrı
−1,465

40
Karaman
−,141

40
Kırşehir
−,034
80
Hakkâri
−1,659

Disclosure

The project “Saving industrial water with using solar power within the framework of artificial intelligence” that was prepared by the corresponding author via utilizing the same methods about water management won the winner prize in UNDP-Every Drops Matter contest in 2014 [43].

Competing Interests

The authors declare that they have no competing interests.

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