Long-term Electrical Load Forecasting based on Economic and Demographic Data for Turkey

Nurettin Cetinkaya Selcuk University, Electrical & Electronics Engineering Department, Konya, Turkey

ncetinkaya@selcuk.edu.tr

Abstract—Load forecasting is very important to operate the electric power systems. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Long term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption data, national incoming, urbanization rate, population increasing rate and as well as other economic parameters. Artificial Neural Network (ANN) and Artificial Neural Fuzzy Inference System (ANFIS) are the famous artificial intelligence methods and have widely used to solve forecasting problems in literature. In this study, artificial intelligence methods and mathematical modeling (MM) are used to forecast long term energy consumption and peak load for Turkey. The four different input data are used to obtain two different outputs in all three methods. Using the four different variables especially in mathematical modeling has been a novelty for Turkey case study. The results obtained from ANFIS, ANN and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used.

I. INTRODUCTION Electrical energy consumption is a momentous data

for technical, social and economic development of a country. Therefore, development and analysis of energy policies are very important. One of the conditions of safe management of the power system is load forecasting. Load forecasting is important for all participants in electric energy generation, transmission, distribution, and markets. Load forecasting can be divided into short-term, mid-term and long-term forecasting. Short-term, mid-term and long-term load forecasts are range from an hour to one week, one week to one year and one year to decades, respectively [1-3]. LTLF take into account the historical load, weather, the number of customers in different categories and other factors [4]. Many LTLF techniques have been proposed used for resource planning and utility expansion in the last 30 years [1-29].

LTLF methods can be categorized in to two main parts: parametric methods and artificial intelligence methods. The artificial intelligence methods are further classified in to neural networks [1, 2, 6, 8-12], genetic algorithms [13], wavelet networks [14], fuzzy logics [15], ANFIS [16], expert system [17] and other methods [18, 19]. The parametric methods are based on connections load demand to its affecting factors like population and income by a mathematical model [3, 20]. The model parameters

are estimated using statistical techniques on electrical load, economic and demographic data. Parametric load forecasting methods can be generally categorized by regression methods and time series prediction methods [21].

Republic of Turkey Energy Market Regulatory Authority (EMRA) provides Turkey electricity market operation in a controlled manner. Regulation of electricity demand forecast was made on 6 April 2006 (Regulation number: 26129). In Turkey, energy consumption projections are made by Ministry of Energy and Natural Resources of Turkey (MENR). Since 1984, MENR performs energy demand forecasts by using Model for Analysis of Energy Demand (MAED) [6]. MAED is an Energy and Power Evaluation Program (ENPEP) module. The full version of ENPEP or some sub-modules are in use nearly 90 countries for energy planning.

MAED forecasts long-term energy demand based on deterministic approach with respect to different scenarios. MAED needs several types of data related to social, economical and demographical structure of country [6, 7]. Above all the electricity price, population growth, employment, climate change, technological developments, price of electrical appliances, etc. are used for electrical load forecasting. Many researchers have studied on forecasting of Turkey’s electricity energy demand and peak load using different methods [21-25].

ANN is used to solve many problems such as load dispatch, fault diagnosis in electric power systems. The use of ANN is an alternative method that is becoming an efficient technique to solve the electrical load forecasting problems. There are researches that treat this problem using the back-propagation algorithm [14]. This algorithm is considered on the specialized literature a benchmark in precision. However, the convergence is slow, although there are some adaptations to improve the performance. Fast processing and achieving admissible results are expected from ANN.

Neuro-fuzzy system, which integrate neural network and fuzzy logic have recently obtained a lot of interest in research and application. A specific approach in neuro-fuzzy development is the ANFIS, which has shown significant results in modeling nonlinear functions [16, 27]. ANFIS is used in many applications and problem solving especially for electric power systems.

In this study, ANN, ANFIS and MM are used to solve energy consumption and peak demand forecasting problems from the year of 2013 to 2032 for Turkey. The values obtained from ANN, ANFIS and MM were compared with MAED values. It is shown that MAED data can achieve more quickly and reliably.

219

CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary

978-1-4799-0197-5/13/$31.00 ©2013 IEEE

Figure 1. ANN Method for LTLF

Figure 2. ANFIS Model for LTLF

II. FORECASTING METHODS

A. ANN Method for LTLF ANN refers to a class of models ingenious by

biological neuron systems. The models are created of many computing units, usually denoted neurons, working in parallel. The simplest and most common network type using supervised learning is a feed-forward network. The network is given an input signal, which is transferred forward through the network. The use of ANN has been widely carried out to electric power system problems especially load forecasting over the last two decades [28, 29]. In order to make a reasonable comparison with the proposed mathematical model, ANN structure is constructed as in Fig. 1.

The ANN structure composes of four inputs and two outputs. One of the inputs of the structure is the total electrical energy consumption by industry (EECI). Other inputs are Gross Domestic Product (GDP) in dollars per month per person, Gross National Income (GNI) in dollars per month per person and population (POP).

In order to make a load forecasting corresponding to exactly one year ahead, the parameters of the previous year are taken as the input, and the load forecasting value of the ‘‘current’’ year’s current year is taken as the output value. Network type is chosen as feed forward back propagation. The Levenberg-Marquard [9] learning algorithm is used in the learning process of ANN.

The data were selected as 70% for training, 15% for validation, and 15% for testing. Training errors for peak demand and energy consumption are 0.055, 0.048, respectively. Testing errors for peak demand and energy consumption are 0.075, 0.087, respectively. Validation errors for peak demand and energy consumption are 0.068, 0.076, respectively.

B. ANFIS Method for LTLF The ANFIS is capable of dealing with uncertainty and

complexity in the given data set and thus provides better solution and estimation with this valuable material. In addition to numerical inputs, ANFIS has linguistics inputs. In this study, due to faster convergence and smaller size training set, ANFIS is intended for use. ANFIS was presented by R. Jang in 1993 [30]. ANFIS is widely used in many kinds of nonlinear problems for engineering applications [31].

ANFIS structure used can be explained in five stages. Stage 1: This layer can be called as fuzzification layer.

Used parameters in this stage is called premise parameters and re-arranged according to output error in every loop. These parameters are membership grades of a fuzzy set and input parameters in this layer.

Stage 2: A fixed node labeled П whose output is the product of all the incoming signals can be computed. Every output of the stage 2 affects the triggering level of the rule in the next stage. Trigger level is called firing strength and П norm operator is called AND operator in fuzzy system.

Stage 3: This layer can be called as normalization layer. For this layer, all firing strengths are re-arranged again by considering own weights.

Stage 4: Defuzzication, this layer is a preliminary calculation of the output for real world. This layer has adaptive nodes and it is expressed as functions and if ANFIS model is Sugeno type then is valid calculation styles turn to linear approach. This type is called first order Sugeno type [32].

Stage 5: Summation neuron; this layer is a fixed node, which computes the overall output as the summation of all incoming signals. ANFIS Learning Ability; ANFIS has two times error correction ability in one loop. This correction is processed through backward and forward. For the backward correction, the antecedent parameters are tuned while the consequent parameters are kept fixed. Least square estimator regulates the parameters to minimize the squared error.

ANFIS structure used is shown in Fig. 2 below [16].

In this study, four inputs are used and two outputs were obtained with ANFIS. Data set is not normalized to obtain real response from ANFIS structure. Natural condition of data set has been kept and used low ANFIS rule structure to obtain fast response. ANFIS setting are configured as range of influence 0.8, squash factor 2.5, reject ratio 0.1, and accept ratio is 0.2. Range of influence and squash factor are increased to obtain low level ANFIS rule structure and to decrease training cycles.

C. Mathematical Method for LTLF Proposed mathematical model uses economic data,

demographic data and electrical energy consumption projections prepared for the future. In this study, the peak load demand, electrical energy consumption by industry, income (GDP, GNI), population, population projections, electrical energy projections and income projections were used to forecast. As a result, between 2013 and 2032, peak load demand and total energy consumption has been forecasted. Proposed MM formulations are given below.

N. Cetinkaya • Long-term Electrical Load Forecasting based on Economic and Demographic Data for Turkey

220

Figure 3. POP and EECI data

Figure 4. GDP and GNI data

Figure 5. Peak and Energy data

The MM’s to forecast total energy consumption (FE(x)) and peak load demand (FP(x)) using Fig. 3, Fig. 4 and Fig. 5 data are given in (1) and (2).

Equations (1) and (2) by curve fitting method using between the years of 1993-2012 data were created.

FE (x) = -55625 + 1.05x (POP) + 1.23x (GDP) + 1.15x (GNI + 1.78x (EECI) (1) FP (x) = -23568 + 0.43x (POP) + 0,535x (GDP) + 0.48x (GNI) + 0.23x (EECI) (2)

III. INPUT DATA AND NUMERICAL RESULTS POP and EECI data used for energy consumption and

peak load demand forecasting are given in Fig. 3.

GDP and GNI data are given in Fig. 4.

Peak demand and energy consumption data from the year of 1993 to 2012 are given in Fig. 5.

Some conditions are required small numbers and small quantities. In this case MAE and its derivatives may lead misunderstanding or may not explain correctly. MAE is one of the simplest ways to evaluate any success and depends on mean of difference among observations and real values [33]. MAE is shown in (3).

∑=

−=n

iipim xx

nMAE

1

1 (3)

where xim is the ith measured value and xip is the forecasted value.

MAPE is used to support of MAE results. MAPE, shown in (4), has no unit and very common in energy forecasting applications.

∑=

−×=

n

i im

ipim

xxx

nMAPE

1

1100 (4)

where xim is the ith measured value and xip is the forecasted value.

Peak load demand forecasting and energy consumption forecasting of the MAED, ANN, ANFIS and MM from the years of 2013 to 2021 are presented together in Table I and Table II, respectively. MAED data are available to 2021 [34].

Error levels for MAE and MAPE are given in Table III. The obtained MAPE results for peak load based on ANFIS, ANN and MM are 0.528, 0.722 and 1.915,

TABLE I. PEAK DEMAND COMPARISONS

MAED ANN ANFIS MM

2013 41000 42100 41564 42321

2014 43800 44352 43979 45508

2015 46800 47500 47420 48442

2016 50210 51254 51045 51558

2017 53965 54987 54560 55277

2018 57980 59123 58435 59254

2019 62265 62900 62678 62906

2020 66845 67021 67050 67256

2021 71985 72013 72176 72065

TABLE II. ENERGY CONSUMPTION COMPARISONS

MAED ANN ANFIS MM

2013 262010 263872 263035 269561

2014 281850 284523 283026 289785

2015 303140 304622 304542 310300

2016 325920 326950 326850 332196

2017 350300 351250 351273 356569

2018 376350 377155 377160 382613

2019 404160 404872 405082 408975

2020 433900 434764 434750 438252

2021 467260 467995 468014 470915

221

CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary

Figure 6. Peak Comparisons

Figure 7. Energy Comparisons

respectively. The obtained MAPE results for total energy consumption based on ANFIS, ANN and MM are 0.209, 0.266 and 1.198, respectively. ANN, ANFIS and MM peak demand and energy consumption data are given for between 2022 and 2032 in Fig. 6 and Fig. 7, respectively.

According to MAE values, peak load demand was estimated to be more accurate from energy consumption. According to MAPE values, energy consumption was estimated to be more accurate from peak load demand.

IV. CONCLUSIONS LTLF is one of the famous power system problems in

literature and has no linear correlation among the input variables. Among the variables used to LTLF also affect each other, therefore, difficult study to make an accurately LTLF. This study is one of the latest studies used Turkey economic and demographic data. The more LTLF is making accurately, the more planning and investment are obtained accurately. This study is intended to benefit the national economy. Therefore, ANN, ANFIS and MM are

used to evaluate real energy demand through the years. Proposed method based on ANFIS is extended and created by background data. Data structure was windowed as six parts from last four years, present year and future year.

After data turned to new time series ANFIS structure is used to evaluate. ANFIS has low error level especially for the future years load forecasting. Average forecasting error is 908.1 GWh, although energy consumption vary from 263035 to 818975 GWh. Average forecasting error is 327.8 MW, although peak load demands vary from 41564 to 125459 MW. ANFIS structure and settings can be changed in future studies for LTLF. After the changes, success of the study should be evaluated by calculating MAE and MAPE. Proposed MM finds easier and faster solution. However, forecasting success is lower than ANN and ANFIS. Average energy consumption forecasting error is 5253.15 GWh. Average peak load forecasting error is 1479.05 MW. Need to increase the number of input data in order to increase the success of the MM. Considering with results which obtained in this study, we can say to use the proposed ANFIS structure is more suitable and more accurate than ANN and MM.

ACKNOWLEDGMENT This work is supported by the Coordinatorship of

Selcuk University’s Scientific Research Projects. (Project number: 13701614).

REFERENCES [1] I. Badran, H. El-Zayyat, and G. Halasa, “Short-Term and

Medium-Term Load Forecasting for Jordan's Power System,” American Journal of Applied Sciences, vol. 5 (7), pp. 763-768, 2008.

[2] B. Kermanshahi and H. Iwamiya, ”Up to year 2020 load forecasting using neural nets,” Electrical Power and Energy Systems, vol.24, pp. 789-797, 2002.

[3] H. M. Al-Hamadi and S.A. Soliman, “Long-term/mid-term electric load forecasting based on short-term correlation and annual growth,” Electric Power Systems Research, vol. 74, pp. 353–361, April 2005.

[4] Z. Haydari, F. Kavehnia, M. Askari, and M. Ganbariyan, “Time-series load modelling and load forecasting using neuro-fuzzy techniques,” 9th International Conference. Electrical Power Quality and Utilisation, Barcelona, Spain, pp. 1-6, October 2007.

[5] M. D. Toksari, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: Case of Turkey,” Energy Policy, vol. 37, pp. 1181-1187, 2009.

[6] C. Hamzacebi, “Forecasting of Turkey’s net electricity energy consumption on sectoral bases,” Energy Policy, vol. 35, pp. 2009-2016, 2007.

[7] A. Unler, “Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025” Energy Policy, vol. 36, pp. 1937-1944, 2008.

[8] I. Eke, “Medium-Term Load Forecasting By Artificial Neural Network Based Differential Evolution,” International Journal of Research and Development, vol. 3, no.1, pp. 28-32, January 2011.

[9] U. B. Filik, O. N. Gerek, and M. Kurban, “Neural and Mathematical Modeling Approaches for Hourly Long Term Load Forecasting,” ICIC Express Letters, vol. 3, pp. 1-6, 2009.

[10] I. Badar, “Comparison of Conventional and Modern Load Forecasting Techniques Based on Artificial Intelligence and Expert Systems,” International Journal of Computer Science Issues, vol. 8, no. 3, pp. 504-513, September 2011.

[11] S. Badran and O. Abouelatta, “Neural Network Integrated with Regression Methods to Forecast Electrical Load,” International Conference on Electrical, Electronics and Biomedical Engineering, Penang Malaysia, pp. 60-65, May 2012.

TABLE III. MAE AND MAPE COMPARISONS

MAE MAPE

peak energy peak energy

MM 1479.05 5253.15 1.915 1.198

ANN 405.15 1106.75 0.722 0.266

ANFIS 327.80 908.10 0.528 0.209

N. Cetinkaya • Long-term Electrical Load Forecasting based on Economic and Demographic Data for Turkey

222

[12] D.C. Park, M.A. El-Sharkawi, R.J. Marks II, L.E. Atlas, and M.J. Damborg, “Electric load forecasting using an artificial neural network,” IEEE Transactions on Power Engineering, vol. 6, pp. 442-449, 1991.

[13] A. Gupta and P. K. Sarangi, “Electrical Load Forecasting Using Genetic Algorithm Based Back- Propagation Method,” Journal of Engineering and Applied Sciences, vol. 7, no. 8, pp. 1017-1020, August 2012.

[14] L. Ghods and M. Kalantar, “Different Methods of Long-Term Electric Load Demand Forecasting; A Comprehensive Review,” Iranian Journal of Electrical & Electronic Engineering, vol. 7, no. 4, pp. 249-259, Dec. 2011.

[15] S. Sachdeva, M. Singh, U. P. Singh, and A. S. Arora, “Efficient Load Forecasting Optimized by Fuzzy Programming and OFDM Transmission,” Advances in Fuzzy Systems, vol. 2011, pp. 1-6, 2011.

[16] B. Akdemir and N. Cetinkaya, “Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data,” Energy Procedia, vol. 14, pp. 794-799, 2012.

[17] M. S. Kandil, S. M. El-Debeiky, and N. E. Hasanien, “Long-Term Load Forecasting for Fast Developing Utility Using a Knowledge-Based Expert System,” IEEE Transactions on Power Systems, vol. 17, no. 2, pp. 491-496, May 2002.

[18] H. K. Alfares and M. Nazeeruddin, “Electric load forecasting: literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23-34, 2002.

[19] Z. Aung, M. Toukhy, J. R. Williams, A. Sanchez, and S. Herrero, “Towards Accurate Electricity Load Forecasting in Smart Grids,” The Fourth International Conference on Advances in Databases, Knowledge, and Data Applications, pp. 51-57, 2012.

[20] L. Ghods and M. Kalantar, “Methods for long-term electric load demand forecasting; a comprehensive investigation,” ICIT 2008, IEEE International Conference on Industrial Technology, China, pp. 1-4, April 2008.

[21] V. Ediger and H. Tatlidil, “Forecasting the primary energy demand in Turkey and analysis of cyclic patterns,” Energy Conversion and Management, vol. 43, pp. 473-487, 2002.

[22] H. Ceylan and H.K. Ozturk, ”Estimating Energy Demand of Turkey Based on Economic Indicators Using Genetic Algorithm Approach,” Energy Conversion and Management, vol. 45, pp. 2525-2537, 2004.

[23] H. K. Ozturk, H. Ceylan, O. E. Canyurt, and A. Hepbasli, “Electricity estimation using genetic algorithm approach: a case study of Turkey,” Energy, vol. 30(7), pp. 1003-1012, 2005.

[24] A. Sozen, E Arcaklioglu, and M. Ozkaymak, “Turkey’s Net Energy Consumption,” Applied Energy, vol. 81(2), pp. 209-221, 2005.

[25] U. B. Filik, O. N. Gerek, and M. Kurban, “A novel modelling approach for hourly forecasting of long-term electric energy demand,” Energy Conversion and Management, vol. 52, pp. 199-211, 2011.

[26] N. F. A. Ghani, M. M. Othman, S. R. Kasim, I. Musirin, A. Mohamed, and A. Hussain, “Determination of Composite Power System Expansion Planning,” International Conference on Intelligent and Advanced Systems, Malaysia, pp. 1-5, June 2010.

[27] V. Nadimi, A. Azadeh, P. Pazhoheshfar, and M. Saberi,” An Adaptive-Network-Based Fuzzy Inference System for Long-Term Electric Consumption Forecasting (2008-2015): A Case Study of the Group of Seven (G7) Industrialized Nations: U.S.A, Canada, Germany, United Kingdom, Japan, France and Italy,” UKSim Fourth European Modelling Symposium on Computer Modelling and Simulation, Italy, pp. 301-305, Nov. 2010.

[28] U. B. Filik, O. N. Gerek, and M. Kurban, “Hourly forecasting of long term electric energy demand using novel mathematical models and neural networks,” International journal of Innovative Computing, Information and Control, vol. 7, pp. 3545-3557, 2011.

[29] M. L. Lu´cia, C. R. Minussi, and A. P. Diva, “Electric load forecasting using a fuzzy ART&ARTMAP neural network,” Applied Soft Computing, vol. 5(2), pp. 235-244, 2005.

[30] J-S.R. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on System, Man and Cybernetics, vol. 23(3), pp. 665-685, 1993.

[31] R. Jang, C. T. Sun, and E. Mizutani, “Neuro-fuzzy and soft computing,” PTR Prentice Hall, 1997.

[32] Y. Narukawa, T. Murofushi, and M. Sugeno, “Regular fuzzy measure and representation of comonotonically additive functional,” Fuzzy Sets and Systems, vol. 112, pp. 177-186, 2000.

[33] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” International Journal of Forecasting, vol. 22(4), pp. 679-688, 2006.

[34] http://www.teias.gov.tr/projeksiyon/KapasiteProjeksiyonu2011. pdf, 2012.

223

CINTI 2013 • 14th IEEE International Symposium on Computational Intelligence and Informatics • 19–21 November, 2013 • Budapest, Hungary