Abstract
Electricity consumption has been on a rapid increase worldwide and it is a very vital component of human life in this age. Hence, reliable supply of electricity from the utility operators is a necessity. However, the constraints that electricity supplied must be the same as electricity consumed puts the burden on the utility operators to make sure that demand is equal to supply at any point in time in smart and connected communities. Load forecasting techniques, therefore, aim to resolve these challenges for the operators by providing accurate forecasts of electrical load demand. This paper reviews current and mostly used short term forecasting techniques, drawing parallels be-tween them; and highlighting their advantages and disadvantages. This paper concludes by stating that there is no one-size-fits-all technique for load forecasting problems, as appropriate techniques depend on several factors such as data size and variability and environmental variables. Different optimization techniques can be used whether to reduce errors and its variations or to speed up computational time, hence resulting in an improved model. However, it is imperative to consider the tradeoffs between each model and its different variants in the context of smart and connected communities.
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References
Wolde-Rufael, Y.: Electricity consumption and economic growth: a time series experience for 17 African countries. Energy Policy 34(10), 1106–1114 (2006)
Lu, W.-C.: Electricity consumption and economic growth: evidence from 17 Taiwanese industries. Sustainability 9(1), 1–15 (2016)
Mohan, N., Soman, K.P., Sachin Kumar, S.: A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model. Appl. Energy 232, 229–244 (2018)
Amara, F., et al.: Household electricity demand forecasting using adaptive conditional density estimation. Energy Build. 156, 271–280 (2017)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)
Luger, G.F.: Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6th edn. Pearson (2009)
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory (1992)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)
Kuster, C., Rezgui, Y., Mourshed, M.: Electrical load forecasting models: a critical systematic review. Sustain. Cities Soc. 35, 257–270 (2017)
Auria, L., Moro, R.A.: Support Vector Machines (SVM) as a Technique for Solvency Analysis. DIW Berlin, German Institute for Economic Research (2008)
Zhao, H.X., Magoulés, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16(6), 3586–3592 (2012)
Hong, T., Fan, S.: Probabilistic electric load forecasting: a tutorial review. Int. J. Forecast. 32(3), 914–938 (2016)
Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting (2013)
Smola, A.J., Schölkopf, B.: A tutorial on support vector regression. Stat. Comput. 14(3), 199–222 (2004)
Elias, C.N., Hatziargyriou, N.D.: An annual midterm energy forecasting model using fuzzy logic. IEEE Trans. Power Syst. 24(1), 469–478 (2009)
Chen, S.X., Gooi, H.B., Wang, M.Q.: Solar radiation forecast based on fuzzy logic and neural networks. Renew. Energy 60, 195–201 (2013)
Huang, G.-B., Zhu, Q.-Y., Siew, C.-K.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: IEEE International Joint Conference (2004)
Zhu, Q.-Y., et al.: Evolutionary extreme learning machine. Pattern Recogn. 38(10), 1759–1763 (2005)
Tissera, M.D., McDonnell, M.D.: Deep extreme learning machines: supervised autoencoding architecture for classification. Neurocomputing 174, 42–49 (2016)
Chae, Y.T., et al.: Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. Energy Build. 111, 184–194 (2016)
Moazzami, M., Khodabakhshian, A., Hooshmand, R.: A new hybrid day-ahead peak load forecasting method for Iran’s National Grid. Appl. Energy 101, 489–501 (2013)
Hu, R., et al.: A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221, 24–31 (2017)
Khwaja, A.S., et al.: Improved short-term load forecasting using bagged neural networks. Electr. Power Syst. Res. 125, 109–115 (2015)
Khwaja, A.S., et al.: Boosted neural networks for improved short-term electric load forecasting. Electr. Power Syst. Res. 143, 431–437 (2017)
Zhang, J., et al.: Enhancing performance of the backpropagation algorithm via sparse response regularization. Neurocomputing 153, 20–40 (2015)
Ozerdem, O.C., Olaniyi, E.O., Oyedotun, O.K.: Short term load forecasting using particle swarm optimization neural network. Procedia Comput. Sci. 120, 382–393 (2017)
Guo, Z., et al.: A deep learning model for short-term power load and probability density forecasting. Energy 160, 1186–1200 (2018)
He, W.: Load forecasting via deep neural networks. Procedia Comput. Sci. 122, 308–314 (2017)
Muralitharan, K., Sakthivel, R., Vishnuvarthan, R.: Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing 273, 199–208 (2018)
Chitsaz, H., et al.: Short-term electricity load forecasting of buildings in microgrids. Energy Build. 99, 50–60 (2015)
Rana, M., Koprinska, I.: Forecasting electricity load with advanced wavelet neural networks. Neurocomputing 182, 118–132 (2016)
Rahman, A., Srikumar, V., Smith, A.D.: Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks. Appl. Energy 212, 372–385 (2018)
Koschwitz, D., Frisch, J., van Treeck, C.: Data-driven heating and cooling load predictions for non-residential buildings based on support vector machine regression and NARX Recurrent Neural Network: a comparative study on district scale. Energy 165, 134–142 (2018)
Ruiz, L.G.B., et al.: Energy consumption forecasting based on Elman neural networks with evolutive optimization. Expert Syst. Appl. 92, 380–389 (2018)
Ko, C.-N., Lee, C.-M.: Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49, 413–422 (2013)
Che, J., Wang, J.: Short-term load forecasting using a kernel-based support vector regression combination model. Appl. Energy 132, 602–609 (2014)
Chen, Y., Tan, H., Song, X.: Day-ahead forecasting of non-stationary electric power demand in commercial buildings: hybrid support vector regression based. Energy Procedia 105, 2101–2106 (2017)
Yang, W., Kang, C., Xia, Q., et al.: Short-term probabilistic load forecasting based on statistics of probability distribution of forecasting errors. Autom. Electr. Power Syst. 30(19), 47–52 (2006)
Niu, D., Wang, Y., Wu, D.D.: Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl. 37(3), 2531–2539 (2010)
Tong, C., et al.: An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J. Parallel Distrib. Comput. 117, 267–273 (2018)
Vrablecová, P., et al.: Smart grid load forecasting using online support vector regression. Comput. Electr. Eng. 65, 102–117 (2018)
Li, Y., Che, J., Yang, Y.: Subsampled support vector regression ensemble for short term electric load forecasting. Energy 164, 160–170 (2018)
Zheng, Y., Zhu, L., Zou, X.: Short-term load forecasting based on Gaussian wavelet SVM. Energy Procedia 12, 387–393 (2011)
Zhang, X., Wang, J.: A novel decomposition-ensemble model for forecasting short-term load-time series with multiple seasonal patterns. Appl. Soft Comput. 65, 478–494 (2018)
Zeng, X.-Y., et al.: Triangular fuzzy series forecasting based on grey model and neural network. Appl. Math. Model. 40(3), 1717–1727 (2016)
Coelho, V.N., et al.: A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment. Appl. Energy 169, 567–584 (2016)
Liu, N., et al.: A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids. Appl. Energy 129, 336–345 (2014)
Sala-Cardoso, E., et al.: Activity-aware HVAC power demand forecasting. Energy Build. 170, 15–24 (2018)
Chen, Y., et al.: Mixed kernel based extreme learning machine for electric load forecasting. Neurocomputing 312, 90–106 (2018)
Jiang, P., Liu, F., Song, Y.: A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy 119, 694–709 (2017)
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Alagbe, V., Popoola, S.I., Atayero, A.A., Adebisi, B., Abolade, R.O., Misra, S. (2019). Artificial Intelligence Techniques for Electrical Load Forecasting in Smart and Connected Communities. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11623. Springer, Cham. https://doi.org/10.1007/978-3-030-24308-1_18
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DOI: https://doi.org/10.1007/978-3-030-24308-1_18
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