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Title: An advanced deep neuroevolution model for probabilistic load forecasting
Authors: Jalali, Seyed M.J.
Arora, Paul
Panigrahi, B.K.
Khosravi, Abbas
Najavandi, Saeid
Osório, Gerardo J.
Catalão, João P.S.
Keywords: Deep learning
Probabilistic load forecasting
Issue Date: 13-Jul-2022
Publisher: Elsevier
Citation: Jalali, S. M. J., Arora, P., Panigrahi, B. K., Khosravi, A., Najavandi, S., Osório, G. J., & Catalão, J. P. S. (2022). An advanced deep neuroevolution model for probabilistic load forecasting. Electric Power Systems Research, 211(Article ID 108351), 1-7. Repositório Institucional UPT.
Abstract: Probabilistic load forecasting (PLF) is necessary for power system operations and control as it assists in proper scheduling and dispatch. Moreover, PLF adequately captures the uncertainty whether that uncertainty is related to load data or the forecasting model. And there are not many PLF models, and those which exist are very complex or difficult to interpret. This paper proposes a novel neuroevolution algorithm for handling the uncertainty associated with load forecasting. In this paper, a new modified evolutionary algorithm is proposed which is used to find the optimal hyperparameters of 1D-Convolutional neural network (CNN). The probabilistic forecasts are produced by minimizing the mean scaled interval score loss function at 50%, 90% and 95% prediction intervals. The proposed neuroevolution algorithm is tested on a global energy forecasting competition (GEFCom-2014) load dataset, and two different experiments are conducted considering load only and one with load and temperature. Strong conclusions are drawn from these experiments. Also, the proposed model is compared with other benchmark models, and it has been shown that it outperforms the other models.
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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