A Platform Independent Web-Application for Short-Term Electric Power Load Forecasting on a 33/11 kV Substation Using Regression Model

Short-term electric power load forecasting is a critical and essential task for utilities of the elec- tric power industry for proper energy trading and that enable the independent system operator to operate the network without any technical and economical is- sues. In this paper, machine learning model such as linear regression model is used to forecast the active power load one hour and one day ahead. Real time active power load data to train and test the machine learning model is collected from a 33/11 kV substation located in Telangana State, India. Based on the simu- lation results, it is observed that linear regression model can forecast the load with less mean absolute error i.e. 0.042 with training data and 0.045 with testing data in comparison with support vector regressor model for an hour ahead operation. Whereas in the case of the day ahead operation, linear regression model can forecast the load with less mean absolute error i.e. 0.055 with training data and 0.057 with testing data in comparison with support vector regressor model. A platform independent web application is developed to help the operators of the 33/11 kV substation which is located in Godishala, Telangana State, India

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies. 6(3):1385-1408. doi:10.3390/en6031385S1385140863Booklets European Comission. Your Guide to the Lisbon Treaty 2009http://ec.europa.eu/publications/booklets/others/84/en.pdfHernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446http://ec.europa.eu/energy/technology/set_plan/set_plan_en.htmFUTURED—Spanish Technological Platform for Energy Grids Home Pagehttp://www.futured.es/European Technology Platform for Electricity Networks of the Future—SmartGrids ETP Home Pagehttp://www.smartgrids.eu/Kim, H.-M., Kinoshita, T., & Shin, M.-C. (2010). A Multiagent System for Autonomous Operation of Islanded Microgrids Based on a Power Market Environment. Energies, 3(12), 1972-1990. doi:10.3390/en3121972Kim, H.-M., Lim, Y., & Kinoshita, T. (2012). An Intelligent Multiagent System for Autonomous Microgrid Operation. Energies, 5(9), 3347-3362. doi:10.3390/en5093347Xiao, Z., Li, T., Huang, M., Shi, J., Yang, J., Yu, J., & Wu, W. (2010). Hierarchical MAS Based Control Strategy for Microgrid. Energies, 3(9), 1622-1638. doi:10.3390/en3091622Hong, Y.-Y., & Chou, J.-H. (2012). Nonintrusive Energy Monitoring for Microgrids Using Hybrid Self-Organizing Feature-Mapping Networks. Energies, 5(7), 2578-2593. doi:10.3390/en5072578Douglas, A. P., Breipohl, A. M., Lee, F. N., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298Sadownik, R., & Barbosa, E. P. (1999). Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215-224. doi:10.1002/(sici)1099-131x(199905)18:33.0.co;2-bHuang, S. R. (1997). Short-term load forecasting using threshold autoregressive models. IEE Proceedings - Generation, Transmission and Distribution, 144(5), 477. doi:10.1049/ip-gtd:19971144Infield, D. G., & Hill, D. C. (1998). Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Transactions on Power Systems, 13(3), 1115-1120. doi:10.1109/59.709108Sargunaraj, S., Sen Gupta, D. P., & Devi, S. (1997). Short-term load forecasting for demand side management. IEE Proceedings - Generation, Transmission and Distribution, 144(1), 68. doi:10.1049/ip-gtd:19970599Hong-Tzer Yang, & Chao-Ming Huang. (1998). A new short-term load forecasting approach using self-organizing fuzzy ARMAX models. IEEE Transactions on Power Systems, 13(1), 217-225. doi:10.1109/59.651639Hong-Tzer Yang, Chao-Ming Huang, & Ching-Lien Huang. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408. doi:10.1109/59.486125Yu, Z. (1996). A temperature match based optimization method for daily load prediction considering DLC effect. IEEE Transactions on Power Systems, 11(2), 728-733. doi:10.1109/59.496146Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725-730. doi:10.1109/59.708572Taylor, J. W., & Majithia, S. (2000). Using combined forecasts with changing weights for electricity demand profiling. Journal of the Operational Research Society, 51(1), 72-82. doi:10.1057/palgrave.jors.2600856Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13(2), 161-174. doi:10.1016/s0169-2070(97)00015-0Soliman, S. A., Persaud, S., El-Nagar, K., & El-Hawary, M. E. (1997). Application of least absolute value parameter estimation based on linear programming to short-term load forecasting. International Journal of Electrical Power & Energy Systems, 19(3), 209-216. doi:10.1016/s0142-0615(96)00048-8Hyde, O., & Hodnett, P. F. (1997). An adaptable automated procedure for short-term electricity load forecasting. IEEE Transactions on Power Systems, 12(1), 84-94. doi:10.1109/59.574927Ku-Long Ho, Yuan-Yih Hsu, Chuan-Fu Chen, Tzong-En Lee, Chih-Chien Liang, Tsau-Shin Lai, & Kung-Keng Chen. (1990). Short term load forecasting of Taiwan power system using a knowledge-based expert system. IEEE Transactions on Power Systems, 5(4), 1214-1221. doi:10.1109/59.99372Rahman, S., & Hazim, O. (1993). A generalized knowledge-based short-term load-forecasting technique. IEEE Transactions on Power Systems, 8(2), 508-514. doi:10.1109/59.260833Mori, H., & Kobayashi, H. (1996). Optimal fuzzy inference for short-term load forecasting. IEEE Transactions on Power Systems, 11(1), 390-396. doi:10.1109/59.486123Bakirtzis, A. G., Theocharis, J. B., Kiartzis, S. J., & Satsios, K. J. (1995). Short term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 10(3), 1518-1524. doi:10.1109/59.466494Papadakis, S. E., Theocharis, J. B., Kiartzis, S. J., & Bakirtzis, A. G. (1998). A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 13(2), 480-492. doi:10.1109/59.667372Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s120201468Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L. M., Aguiar, J. M., & Carro, B. (2012). Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks. Sensors, 12(10), 14004-14021. doi:10.3390/s121014004Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449. doi:10.1109/59.76685Ho, K. L., Hsu, Y.-Y., & Yang, C.-C. (1992). Short term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power Systems, 7(1), 141-149. doi:10.1109/59.141697Drezga, I., & Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1238-1244. doi:10.1109/59.736244Drezga, I., & Rahman, S. (1999). Short-term load forecasting with local ANN predictors. IEEE Transactions on Power Systems, 14(3), 844-850. doi:10.1109/59.780894Lee, K. Y., Cha, Y. T., & Park, J. H. (1992). Short-term load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 7(1), 124-132. doi:10.1109/59.141695Lu, C.-N., Wu, H.-T., & Vemuri, S. (1993). Neural network based short term load forecasting. IEEE Transactions on Power Systems, 8(1), 336-342. doi:10.1109/59.221223Papalexopoulos, A. D., Shangyou Hao, & Tie-Mao Peng. (1994). An implementation of a neural network based load forecasting model for the EMS. IEEE Transactions on Power Systems, 9(4), 1956-1962. doi:10.1109/59.331456Bakirtzls, A. G., Petridls, V., Klartzis, S. J., Alexladls, M. C., & Malssls, A. H. (1996). A neural network short term load forecasting model for the Greek power system. IEEE Transactions on Power Systems, 11(2), 858-863. doi:10.1109/59.496166AlFuhaid, A. S., El-Sayed, M. A., & Mahmoud, M. S. (1997). Cascaded artificial neural networks for short-term load forecasting. IEEE Transactions on Power Systems, 12(4), 1524-1529. doi:10.1109/59.627852Lamedica, R., Prudenzi, A., Sforna, M., Caciotta, M., & Cencellli, V. O. (1996). A neural network based technique for short-term forecasting of anomalous load periods. IEEE Transactions on Power Systems, 11(4), 1749-1756. doi:10.1109/59.544638Srinivasan, D. (1994). Forecasting daily load curves using a hybrid fuzzy-neural approach. IEE Proceedings - Generation, Transmission and Distribution, 141(6), 561. doi:10.1049/ip-gtd:19941288Kwang-Ho Kim, Jong-Keun Park, Kab-Ju Hwang, & Sung-Hak Kim. (1995). Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Transactions on Power Systems, 10(3), 1534-1539. doi:10.1109/59.466492Daneshdoost, M., Lotfalian, M., Bumroonggit, G., & Ngoy, J. P. (1998). Neural network with fuzzy set-based classification for short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1386-1391. doi:10.1109/59.736281Senjyu, T., Mandal, P., Uezato, K., & Funabashi, T. (2005). Next Day Load Curve Forecasting Using Hybrid Correction Method. IEEE Transactions on Power Systems, 20(1), 102-109. doi:10.1109/tpwrs.2004.831256Hsu, Y.-Y., & Yang, C.-C. (1991). Design of artificial neural networks for short-term load forecasting. Part 2: Multilayer feedforward networks for peak load and valley load forecasting. IEE Proceedings C Generation, Transmission and Distribution, 138(5), 414. doi:10.1049/ip-c.1991.0052Rejc, M., & Pantos, M. (2011). Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE Transactions on Power Systems, 26(3), 1511-1521. doi:10.1109/tpwrs.2010.2096829Wang, Y., Xia, Q., & Kang, C. (2011). Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting. IEEE Transactions on Power Systems, 26(2), 500-507. doi:10.1109/tpwrs.2010.2052638Kebriaei, H., Araabi, B. N., & Rahimi-Kian, A. (2011). Short-Term Load Forecasting With a New Nonsymmetric Penalty Function. IEEE Transactions on Power Systems, 26(4), 1817-1825. doi:10.1109/tpwrs.2011.2142330Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. doi:10.1063/1.1144830Taylor, J. W., & Buizza, R. (2002). Neural network load forecasting with weather ensemble predictions. IEEE Transactions on Power Systems, 17(3), 626-632. doi:10.1109/tpwrs.2002.800906Chu, W.-C., Chen, Y.-P., Xu, Z.-W., & Lee, W.-J. (2011). Multiregion Short-Term Load Forecasting in Consideration of HI and Load/Weather Diversity. IEEE Transactions on Industry Applications, 47(1), 232-237. doi:10.1109/tia.2010.2090440Zhang, W. Y., Hong, W.-C., Dong, Y., Tsai, G., Sung, J.-T., & Fan, G. (2012). Application of SVR with chaotic GASA algorithm in cyclic electric load forecasting. Energy, 45(1), 850-858. doi:10.1016/j.energy.2012.07.006Hong, W.-C. (2011). Electric load forecasting by seasonal recurrent SVR (support vector regression) with chaotic artificial bee colony algorithm. Energy, 36(9), 5568-5578. doi:10.1016/j.energy.2011.07.015Nose-Filho, K., Lotufo, A. D. P., & Minussi, C. R. (2011). Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network. IEEE Transactions on Power Delivery, 26(4), 2862-2869. doi:10.1109/tpwrd.2011.2166566Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Good, R. P., Kost, D., & Cherry, G. A. (2010). Introducing a Unified PCA Algorithm for Model Size Reduction. IEEE Transactions on Semiconductor Manufacturing, 23(2), 201-209. doi:10.1109/tsm.2010.2041263Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169http://www.ree.es/sistema_electrico/pdf/indel/Atlas_INDEL_REE.pdfHsu, C.-C., & Chen, C.-Y. (2003). Regional load forecasting in Taiwan––applications of artificial neural networks. Energy Conversion and Management, 44(12), 1941-1949. doi:10.1016/s0196-8904(02)00225-

Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter

[EN] Forecasting electricity demand through time series is a tool used by transmission system operators to establish future operating conditions. The accuracy of these forecasts is essential for the precise development of activity. However, the accuracy of the forecasts is enormously subject to the calendar effect. The multiple seasonal Holt-Winters models are widely used due to the great precision and simplicity that they offer. Usually, these models relate this calendar effect to external variables that contribute to modification of their forecasts a posteriori. In this work, a new point of view is presented, where the calendar effect constitutes a built-in part of the Holt-Winters model. In particular, the proposed model incorporates discrete-interval moving seasonalities. Moreover, a clear example of the application of this methodology to situations that are difficult to treat, such as the days of Easter, is presented. The results show that the proposed model performs well, outperforming the regular Holt-Winters model and other methods such as artificial neural networks and Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS) methods.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-8888209C2-1-R.Trull, Ó.; García-Díaz, JC.; Troncoso, A. (2019). Application of Discrete-Interval Moving Seasonalities to Spanish Electricity Demand Forecasting during Easter. Energies. 12(6):1-16. https://doi.org/10.3390/en12061083S116126Garrués-Irurzun, J., & López-García, S. (2009). Red Eléctrica de España S.A.: Instrument of regulation and liberalization of the Spanish electricity market (1944–2004). Renewable and Sustainable Energy Reviews, 13(8), 2061-2069. doi:10.1016/j.rser.2009.01.028Roldan-Fernandez, J., Gómez-Quiles, C., Merre, A., Burgos-Payán, M., & Riquelme-Santos, J. (2018). Cross-Border Energy Exchange and Renewable Premiums: The Case of the Iberian System. Energies, 11(12), 3277. doi:10.3390/en11123277Contreras, J., Espinola, R., Nogales, F. J., & Conejo, A. J. (2003). ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems, 18(3), 1014-1020. doi:10.1109/tpwrs.2002.804943Juberias, G., Yunta, R., Garcia Moreno, J., & Mendivil, C. (1999). A new ARIMA model for hourly load forecasting. 1999 IEEE Transmission and Distribution Conference (Cat. No. 99CH36333). doi:10.1109/tdc.1999.755371Bianco, V., Manca, O., & Nardini, S. (2009). Electricity consumption forecasting in Italy using linear regression models. Energy, 34(9), 1413-1421. doi:10.1016/j.energy.2009.06.034Taylor, J. W. (2003). Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society, 54(8), 799-805. doi:10.1057/palgrave.jors.2601589Taylor, J. W. (2010). Triple seasonal methods for short-term electricity demand forecasting. European Journal of Operational Research, 204(1), 139-152. doi:10.1016/j.ejor.2009.10.003Ko, C.-N., & Lee, C.-M. (2013). Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy, 49, 413-422. doi:10.1016/j.energy.2012.11.015Rana, M., & Koprinska, I. (2016). Forecasting electricity load with advanced wavelet neural networks. Neurocomputing, 182, 118-132. doi:10.1016/j.neucom.2015.12.004Baliyan, A., Gaurav, K., & Mishra, S. K. (2015). A Review of Short Term Load Forecasting using Artificial Neural Network Models. Procedia Computer Science, 48, 121-125. doi:10.1016/j.procs.2015.04.160Yang, Z., Ce, L., & Lian, L. (2017). Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy, 190, 291-305. doi:10.1016/j.apenergy.2016.12.130Ghadimi, N., Akbarimajd, A., Shayeghi, H., & Abedinia, O. (2018). Two stage forecast engine with feature selection technique and improved meta-heuristic algorithm for electricity load forecasting. Energy, 161, 130-142. doi:10.1016/j.energy.2018.07.088Troncoso Lora, A., Riquelme Santos, J. M., Riquelme, J. C., Gómez Expósito, A., & Martínez Ramos, J. L. (2004). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. Lecture Notes in Computer Science, 577-586. doi:10.1007/978-3-540-25945-9_57Martinez Alvarez, F., Troncoso, A., Riquelme, J. C., & Aguilar Ruiz, J. S. (2011). Energy Time Series Forecasting Based on Pattern Sequence Similarity. IEEE Transactions on Knowledge and Data Engineering, 23(8), 1230-1243. doi:10.1109/tkde.2010.227Cancelo, J. R., Espasa, A., & Grafe, R. (2008). Forecasting the electricity load from one day to one week ahead for the Spanish system operator. International Journal of Forecasting, 24(4), 588-602. doi:10.1016/j.ijforecast.2008.07.005TORRÓ, H., MENEU, V., & VALOR, E. (2003). Single Factor Stochastic Models with Seasonality Applied to Underlying Weather Derivatives Variables. The Journal of Risk Finance, 4(4), 6-17. doi:10.1108/eb022969Darbellay, G. A., & Slama, M. (2000). Forecasting the short-term demand for electricity. International Journal of Forecasting, 16(1), 71-83. doi:10.1016/s0169-2070(99)00045-xMoral-Carcedo, J., & Vicéns-Otero, J. (2005). Modelling the non-linear response of Spanish electricity demand to temperature variations. Energy Economics, 27(3), 477-494. doi:10.1016/j.eneco.2005.01.003Erişen, E., Iyigun, C., & Tanrısever, F. (2017). Short-term electricity load forecasting with special days: an analysis on parametric and non-parametric methods. Annals of Operations Research. doi:10.1007/s10479-017-2726-6Arora, S., & Taylor, J. W. (2013). Short-Term Forecasting of Anomalous Load Using Rule-Based Triple Seasonal Methods. IEEE Transactions on Power Systems, 28(3), 3235-3242. doi:10.1109/tpwrs.2013.2252929Arora, S., & Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for France. European Journal of Operational Research, 266(1), 259-268. doi:10.1016/j.ejor.2017.08.056Bermúdez, J. D. (2013). Exponential smoothing with covariates applied to electricity demand forecast. European J. of Industrial Engineering, 7(3), 333. doi:10.1504/ejie.2013.054134Göb, R., Lurz, K., & Pievatolo, A. (2013). Electrical load forecasting by exponential smoothing with covariates. Applied Stochastic Models in Business and Industry, 29(6), 629-645. doi:10.1002/asmb.2008Chatfield, C. (1978). The Holt-Winters Forecasting Procedure. Applied Statistics, 27(3), 264. doi:10.2307/234716

Long-Term Load Forecasting Considering Volatility Using Multiplicative Error Model

Long-term load forecasting plays a vital role for utilities and planners in terms of grid development and expansion planning. An overestimate of long-term electricity load will result in substantial wasted investment in the construction of excess power facilities, while an underestimate of future load will result in insufficient generation and unmet demand. This paper presents first-of-its-kind approach to use multiplicative error model (MEM) in forecasting load for long-term horizon. MEM originates from the structure of autoregressive conditional heteroscedasticity (ARCH) model where conditional variance is dynamically parameterized and it multiplicatively interacts with an innovation term of time-series. Historical load data, accessed from a U.S. regional transmission operator, and recession data for years 1993-2016 is used in this study. The superiority of considering volatility is proven by out-of-sample forecast results as well as directional accuracy during the great economic recession of 2008. To incorporate future volatility, backtesting of MEM model is performed. Two performance indicators used to assess the proposed model are mean absolute percentage error (for both in-sample model fit and out-of-sample forecasts) and directional accuracy.Comment: 19 pages, 11 figures, 3 table