Electrical Load Forecasting: A methodological overview

  • Authors

    • Medhat Rostum Ministry of Electricity & Energy, Egypt
    • Amr Zamel Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Egypt
    • Hassan Moustafa Ministry of Electricity & Energy, Egypt
    • Ibrahim Ziedan Computer and Systems Engineering Department, Faculty of Engineering, Zagazig University, Egypt
    2020-10-06
    https://doi.org/10.14419/ijet.v9i3.30706
  • Load forecasting, load predictions, load demand
  • Electric load forecasting process plays an extensive role in forecasting future electric load demand and peak load by understanding the previous data. Several researchers proved that, the presence of load forecasting error leads to an increase in operating costs. Thus Accurate electric load forecast is needed for power system security and reliability. It also improves energy efficiency, revenues for the electrical companies and reliable operation of a power system.

    In recent times, there are significant proliferations in the implementation of forecasting techniques. This survey aids readers to summarize and compare the latest predominant researches on electric load forecasting. Besides, it presents the most relevant studies on load forecasting over the last decade and discusses the different methods that are used in load prediction as well as the future directions in this field.

  • References

    1. [1] Hobbs, Benjamin F and Jitprapaikulsarn, Suradet and Konda, Sreenivas and Chankong, Vira and Loparo, Kenneth A and Maratukulam, Dominic J, “Analysis of the value for unit commitment of improved load forecastsâ€, IEEE Transactions on Power Systems, Vol.14, No.4, (1999), pp.1342-1348.

      [2] Bunn, Derek W, “Forecasting loads and prices in competitive power marketsâ€, Proceedings of the IEEE, Vol.88, No.8, (2000), pp.163-169.

      [3] Hippert, Henrique Steinherz and Pedreira, Carlos Eduardo and Souza, Reinaldo Castro, “Neural networks for short-term load forecasting: A review and evaluationâ€, IEEE Transactions on power systems, Vol.16, No.1, (2001), pp.44-55.

      [4] Alfares, Hesham K and Nazeeruddin, Mohammad, “Electric load forecasting: literature survey and classification of methodsâ€, International journal of systems science, Vol.33, No.1, (2002), pp.23-34.

      [5] Singh, D and Singh, SP, “Self organization and learning methods in short term electric load forecasting: a reviewâ€, Electric Power Components and Systems, Vol.30, No.10, (2002), pp.1075-1089.

      [6] Metaxiotis, K and Kagiannas, A and Askounis, D and Psarras, J, “Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcherâ€, Energy conversion and Management, Vol.44, No.9, (2003), pp.1525-1534.

      [7] Taylor, James W and De Menezes, Lilian M and McSharry, Patrick E, “A comparison of univariate methods for forecasting electricity demand up to a day aheadâ€, International Journal of Forecasting, Vol.22, No.2, (2006), pp.1-16.

      [8] Hahn, Heiko and Meyer-Nieberg, Silja and Pickl, Stefan, “Electric load forecasting methods: Tools for decision makingâ€, European journal of operational research, Vol.199, No.3, (2009), pp.902-907.

      [9] Hernandez, Luis and Baladron, Carlos and Aguiar, Javier M and Carro, Bel´en and Sanchez-Esguevillas, Antonio J and Lloret, Jaime and Massana, Joaquim, “A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildingsâ€, IEEE Communications Surveys & Tutorials, Vol.16, No.3, (2014), pp.1460-1495.

      [10] Campo, R and Ruiz, P, “Adaptive weather-sensitive short term load forecastâ€, IEEE Transactions on Power Systems, Vol.2, No.3, (1987), pp.592-598.

      [11] Zhang, Guoqiang and Patuwo, B Eddy and Hu, Michael Y, “Forecasting with artificial neural networks:: The state of the artâ€, International journal of forecasting, Vol.14, No.1, (1998), pp.35-62.

      [12] Hyndman, Rob J and Koehler, Anne B, “Another look at measures of forecast accuracyâ€, International journal of forecasting, Vol.22, No.4, (2006), pp.679-688.

      [13] Guan, Che and Luh, Peter B and Michel, Laurent D and Wang, Yuting and Friedland, Peter B, “Very short-term load forecasting: wavelet neural networks with data pre-filteringâ€, IEEE Transactions on Power Systems, Vol.28, No.1, (2012), pp.30-41.

      [14] Hsiao, Yu-Hsiang, “Household electricity demand forecast based on context information and user daily schedule analysis from meter dataâ€, IEEE Transactions on Industrial Informatics, Vol.11, No.1, (2014), pp.33-43.

      [15] Laouafi, Abderrezak and Mordjaoui, Mourad and Haddad, Salim and Boukelia, Taqiy Eddine and Ganouche, Abderahmane, “Online electricity demand forecasting based on an effective forecast combination methodologyâ€, Electric Power Systems Research, Vol.148, No.1, (2017), pp.35-47.

      [16] Penya, Yoseba K and Borges, Cruz E and Fern´andez, Iv´an, “Short-term load forecasting in non-residential buildingsâ€, IEEE Africon’11, Vol.148, (2011), pp.1-6.

      [17] Koo, Bon-gil and Lee, Heung-seok and Park, Juneho, “A study on short-term electric load forecasting using wavelet transformâ€, IEEE PES Innovative Smart Grid Technologies, Europe, (2014), pp.1-6.

      [18] Khwaja, AS and Naeem, M and Anpalagan, A and Venetsanopoulos, A and Venkatesh, B, “Improved short-term load forecasting using bagged neural networksâ€, Electric Power Systems Research, Vol.125, (2015), pp.109-115.

      [19] Khwaja, AS and Zhang, X and Anpalagan, A and Venkatesh, B, “Boosted neural networks for improved short-term electric load forecastingâ€, Electric Power Systems Research, Vol.143, (2017), pp.431-437.

      [20] Hong, Wei-Chiang, “Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithmâ€, Energy, Vol.36, No.9, (2011), pp.5568-5578.

      [21] Hong, Wei-Chiang and Dong, Yucheng and Lai, Chien-Yuan and Chen, Li-Yueh and Wei, Shih-Yung, “SVR with hybrid chaotic immune algorithm for seasonal load demand forecastingâ€, Energies, Vol.4, No.6, (2011), pp.960-977.

      [22] Feilat, EA and Bouzguenda, M, “Medium-term load forecasting using neural network approachâ€, 2011 IEEE PES Conference on Innovative Smart Grid Technologies-Middle East, (2011), pp.1-5.

      [23] Lee, Woo-Joo and Hong, Jinkyu, “A hybrid dynamic and fuzzy time series model for mid-term power load forecastingâ€, International Journal of Electrical Power & Energy Systems, Vol.64, (2015), pp.1057-1062.

      [24] Al-Hamadi, HM, “Long-term electric power load forecasting using fuzzy linear regression techniqueâ€, IEEE Power Engineering and Automation Conference, Vol.3, (2011), pp.96-99.

      [25] Li, Hongze and Guo, Sen and Zhao, Huiru and Su, Chenbo and Wang, Bao, “Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithmâ€, Energies, Vol.5, No.11, (2012), pp.4430-4445.

      [26] Wang, Jianjun and Li, Li and Niu, Dongxiao and Tan, Zhongfu, “An annual load forecasting model based on support vector regression with differential evolution algorithmâ€, Applied Energy, Vol.94, (2012), pp.65-70.

      [27] Li, Hong-Ze and Guo, Sen and Li, Chun-Jie and Sun, Jing-Qi, “A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithmâ€, Knowledge-Based Systems, Vol.37, (2013), pp.378-387.

      [28] Vu, Dao H and Muttaqi, Kashem M and Agalgaonkar, Ashish P, “Short-term load forecasting using regression based moving windows with adjustable window-sizesâ€, IEEE Industry Application Society Annual Meeting, (2014), pp.1-8.

      [29] Dudek, Grzegorz, “Pattern-based local linear regression models for short-term load forecastingâ€, Electric Power Systems Research, Vol.130, (2016), pp.139-147.

      [30] Taylor, James W, “Short-term load forecasting with exponentially weighted methodsâ€, IEEE Transactions on Power Systems, Vol.27, No.1, (2011), pp.458-464.

      [31] Box, George EP and Jenkins, Gwilym M, “Time series analysis: Forecasting and control San Franciscoâ€, Calif: Holden-Day, (1976).

      [32] Lee, Cheng-Ming and Ko, Chia-Nan, “Short-term load forecasting using lifting scheme and ARIMA modelsâ€, Expert Systems with Applications, Vol.38, No.5, (2011), pp.5902-5911.

      [33] He, Hongming and Liu, Tao and Chen, Ruimin and Xiao, Yong and Yang, Jinfeng, “High frequency short-term demand forecasting model for distribution power grid based on ARIMAâ€, 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Vol.3, No.5, (2012), pp.293-297.

      [34] Matsila, Hulisani and Bokoro, Pitshou, “Load forecasting using statistical time series model in a medium voltage distribution networkâ€, IECON 2018-44th Annual Conference of the IEEE Industrial Electronics Society, (2018), pp.4974-4979.

      [35] Alberg, Dima and Last, Mark, “Short-term load forecasting in smart meters with sliding window-based ARIMA algorithmsâ€, Vietnam Journal of Computer Science, Vol.5, No.3-4, (2018), pp.241-249.

      [36] Almeshaiei, Eisa and Soltan, Hassan, “A methodology for electric power load forecastingâ€, Alexandria Engineering Journal, Vol.50, No.2, (2011), pp.137-144.

      [37] Fan, Shu and Hyndman, Rob J, “Short-term load forecasting based on a semi-parametric additive modelâ€, IEEE Transactions on Power Systems, Vol.27, No.1, (2011), pp.134-141.

      [38] Goude, Yannig and Nedellec, Raphael and Kong, Nicolas, “Local short and middle term electricity load forecasting with semi-parametric additive modelsâ€, IEEE transactions on smart grid, Vol.5, No.1, (2013), pp.440-446.

      [39] Ding, Ni and B´esanger, Yvon and Wurtz, Fr´ed´eric, “Next-day MV/LV substation load forecaster using time series methodâ€, Electric Power Systems Research, Vol.119, (2015), pp.345-354.

      [40] Khosravi, Abbas and Nahavandi, Saeid and Creighton, Doug and Srinivasan, Dipti, “Interval type-2 fuzzy logic systems for load forecasting: A comparative studyâ€, IEEE Transactions on Power Systems, Vol.27, No.3, (2012), pp.1274-1282.

      [41] Mukhopadhyay, P and Mitra, G and Banerjee, S and Mukherjee, G, “Electricity load forecasting using fuzzy logic: Short term load forecasting factoring weather parameterâ€, 2017 7th International Conference on Power Systems (ICPS), (2017), pp.812-819.

      [42] Hernandez, Luis and Baladr´on, Carlos and Aguiar, Javier and Carro, Bel´en and Sanchez-Esguevillas, Antonio and Lloret, Jaime, “Short-term load

      forecasting for microgrids based on artificial neural networksâ€, Energies, Vol.6, No.3, (2013), pp.1385-1408.

      [43] Webberley, Ashton and Gao, David Wenzhong, “Study of artificial neural network based short term load forecastingâ€, 2013 IEEE Power & Energy Society General Meeting, (2013), pp.1-4.

      [44] Rodrigues, Filipe and Cardeira, Carlos and Calado, JoËœao Manuel Ferreira, “The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugalâ€, Energy Procedia, Vol.62, (2014), pp.220-229.

      [45] Zjavka, Ladislav and Sn´aˇsel, V´aclav, “Short-term power load forecasting with ordinary differential equation substitutions of polynomial networksâ€, Electric Power Systems Research, Vol.137, (2016), pp.113-123.

      [46] Warrior, Karun P and Shrenik, M and Soni, Nimish, “Short-term electrical load forecasting using predictive machine learning modelsâ€, 2016 IEEE Annual India Conference (INDICON), (2016), pp.1-6.

      [47] Ertugrul, O¨ mer Faruk, “Forecasting electricity load by a novel recurrent extreme learning machines approachâ€, International Journal of Electrical Power & Energy Systems, Vol.78, (2016), pp.429-435.

      [48] Houimli, Rim and Zmami, Mourad and Ben-Salha, Ousama, “Short-term electric load forecasting in Tunisia using artificial neural networksâ€, Energy Systems, Vol.78, pp.1-19.

      [49] Almalaq, Abdulaziz and Edwards, George, “A review of deep learning methods applied on load forecastingâ€, 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), (2017), pp.511-516.

      [50] Ryu, Seunghyoung and Noh, Jaekoo and Kim, Hongseok, “Deep neural network based demand side short term load forecastingâ€, Energies, Vol.10, No.1, (2016), pp.3.

      [51] Zheng, Jian and Xu, Cencen and Zhang, Ziang and Li, Xiaohua, “Electric load forecasting in smart grids using long-short-term-memory based recurrent neural networkâ€, 2017 51st Annual Conference on Information Sciences and Systems (CISS), (2017), pp.1-6.

      [52] Narayan, Apurva and Hipel, Keith W, “Long short term memory networks for short-term electric load forecastingâ€, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), (2017), pp.2573-2578.

      [53] Liu, Chang and Jin, Zhijian and Gu, Jie and Qiu, Caiming, “Short-term load forecasting using a long short-term memory networkâ€, 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), (2017), pp.1-6.

      [54] Kong, Weicong and Dong, Zhao Yang and Jia, Youwei and Hill, David J and Xu, Yan and Zhang, Yuan, “Short-term residential load forecasting based on LSTM recurrent neural networkâ€, IEEE Transactions on Smart Grid, Vol.10, No.1, (2017), pp.841-851.

      [55] Kong, Weicong and Dong, Zhao Yang and Hill, David J and Luo, Fengji and Xu, Yan, “Short-term residential load forecasting based on resident behaviour learningâ€, IEEE Transactions on Power Systems, Vol.33, No.1, (2017), pp.1087-1088.

      [56] Shi, Heng and Xu, Minghao and Li, Ran, “Deep learning for household load forecasting—A novel pooling deep RNNâ€, IEEE Transactions on Smart Grid, Vol.9, No.5, (2017), pp.5271-5280.

      [57] Vapnik, Vladimir N, “The nature of statistical learningâ€, Theory, (1995).

      [58] Zhang, Zhiheng and Ye, Shijie, “Long term load forecasting and recommendations for china based on support vector regressionâ€, 2011 International conference on information management, innovation management and industrial engineering, Vol.3, (2011), pp.597-602.

      [59] Thokala, Naveen Kumar and Bapna, Aakanksha and Chandra, M Girish, “A deployable electrical load forecasting solution for commercial buildingsâ€, 2018 IEEE International Conference on Industrial Technology (ICIT), (2018).

      [60] Nie, Hongzhan and Liu, Guohui and Liu, Xiaoman and Wang, Yong, “Hybrid of ARIMA and SVMs for short-term load forecastingâ€, Energy Procedia, Vol.16, (2012), pp.1455-1460.

      [61] Ko, Chia-Nan and Lee, Cheng-Ming, “hort-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filterâ€, Energy, Vol.49, (2013), pp.413-422.

      [62] Ebrahimi, Akbar and Moshari, Amir, “Holidays short-term load forecasting using fuzzy improved similar day methodâ€, International transactions on electrical energy systems, Vol.23, No.8, (2013), pp.1254-1271.

      [63] Dudek, Grzegorz, “Short-term load cross-forecasting using pattern-based neural modelsâ€, 2015 16th International Scientific Conference on Electric Power Engineering (EPE), (2015), pp.179-183.

      [64] Li, Song and Goel, Lalit and Wang, Peng, “An ensemble approach for short-term load forecasting by extreme learning machineâ€, Applied Energy, Vol.170, (2016), pp.22-29.

      [65] Bantugon, Mary Joyce T and Gallano, Russel John C, “Short-and long-term electricity load forecasting using classical and neural network based approach: A case study for the Philippinesâ€, 2016 IEEE Region 10 Conference (TENCON), (2016), pp.3822-3825.

      [66] Qiu, Xueheng and Ren, Ye and Suganthan, Ponnuthurai Nagaratnam and Amaratunga, Gehan AJ, “Empirical mode decomposition based ensemble deep learning for load demand time series forecastingâ€, Applied Soft Computing, Vo.54, (2017), pp.246-255.

      [67] Akarslan, Emre and Hocaoglu, Fatih Onur, “A novel short-term load forecasting approach using Adaptive Neuro-Fuzzy Inference Systemâ€, 2018 6th International Istanbul Smart Grids and Cities Congress and Fair (ICSG), (2018), pp.160-164.

      [68] Liu, Feng and Wang, Zhifang, “Electric load forecasting using parallel RBF neural networkâ€, 2013 IEEE Global Conference on Signal and Information Processing, (2013), pp.531-534.

      [69] Xiao, Liye and Xiao, Liyang, “Combined modeling for electrical load forecasting with particle swarm optimizationâ€, 2014 IEEE Workshop on Electronics, Computer and Applications, (2014), pp.395-400.

      [70] Ghayekhloo, M and Menhaj, MB and Ghofrani, M, “A hybrid short-term load forecasting with a new data preprocessing frameworkâ€, Electric Power Systems Research, Vo.119, (2015), pp.138-148.

      [71] Xiao, Liye and Wang, Jianzhou and Hou, Ru and Wu, Jie, “A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecastingâ€, Energy, Vo.82, (2015), pp.524-549.

      [72] Liang, Yi and Niu, Dongxiao and Hong, Wei-Chiang, “Short term load forecasting based on feature extraction and improved general regression neural network modelâ€, Energy, Vo.166, (2019), pp.653-663.

      [73] Ahmad, Ashfaq and Javaid, Nadeem and Mateen, Abdul and Awais, Muhammad and Khan, Zahoor, “Short-term load forecasting in smart grids: an intelligent modular approachâ€, Energies, Vo.12, No.1, (2019), pp.164.

      [74] Gao, Wei and Darvishan, Ayda and Toghani, Mohammad and Mohammadi, Mohsen and Abedinia, Oveis and Ghadimi, Noradin, “Different states of multi-block based forecast engine for price and load predictionâ€, International Journal of Electrical Power & Energy Systems, Vo.104, (2019), pp.423-435.

      [75] Singh, Priyanka and Dwivedi, Pragya and Kant, Vibhor, “A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecastingâ€, Energy.

      [76] Ceperic, Ervin and Ceperic, Vladimir and Baric, Adrijan, “A strategy for short-term load forecasting by support vector regression machinesâ€, IEEE Transactions on Power Systems, Vo.28, No.4, (2013), pp.4356-4364.

      [77] Kavousi-Fard, Abdollah and Samet, Haidar and Marzbani, Fatemeh, “A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecastingâ€, Expert systems with applications, Vo.41, No.13, (2014), pp.6047-6056.

      [78] Selakov, A and Cvijetinovi´c, D and Milovi´c, L and Mellon, S and Bekut, D, “Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbankâ€, Applied Soft Computing, Vo.16, (2014), pp.80-88.

      [79] Yang, Ailing and Li, Weide and Yang, Xuan, “Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machinesâ€, Knowledge-Based Systems, Vo.163, (2019), pp.159–173.

      [80] Barman, Mayur and Choudhury, Nalin Behari Dev, “Season specific approach for short-term load forecasting based on hybrid FA-SVM and similarity conceptâ€, Energy, Vo.174, (2019), pp.886-896.

      [81] Bahrami, Saadat and Hooshmand, Rahmat-Allah and Parastegari, Moein, “Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithmâ€, Energy, Vo.72, (2014), pp.434-442.

      [82] Ray, Papia and Sen, Santanu and Barisal, AK, “Hybrid methodology for short-term load forecastingâ€, 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), (2014), pp.1-6.

      [83] Li, Song and Wang, Peng and Goel, Lalit, “Short-term load forecasting by wavelet transform and evolutionary extreme learning machineâ€, Electric Power Systems Research, Vo.122, (2015), pp.96-103.

      [84] Ziel, Florian, “Modelling and forecasting electricity load using Lasso methodsâ€, 2015 Modern Electric Power Systems (MEPS), (2015), pp.1-6.

      [85] Zeng, Nianyin and Zhang, Hong and Liu, Weibo and Liang, Jinling and Alsaadi, Fuad E, “A switching delayed PSO optimized extreme learning machine for short-term load forecastingâ€, Neurocomputing, Vo.240, (2017), pp.175-182.

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    Rostum, M., Zamel, A., Moustafa, H., & Ziedan, I. (2020). Electrical Load Forecasting: A methodological overview. International Journal of Engineering & Technology, 9(3), 842-869. https://doi.org/10.14419/ijet.v9i3.30706