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• W3159352807 abstract "This paper addresses the mid-term electricity load forecasting problem. Solving this problem is necessary for power system operation and planning as well as for negotiating forward contracts in deregulated energy markets. We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem. Proposed neural network has high expressive power to solve non-linear stochastic forecasting problems with time series including trends, seasonality and significant random fluctuations. At the same time, it is simple to implement and train, it does not require signal preprocessing, and it is equipped with a forecast bias reduction mechanism. We compare our approach against ten baseline methods, including classical statistical methods, machine learning and hybrid approaches, on 35 monthly electricity demand time series for European countries. The empirical study shows that proposed neural network clearly outperforms all competitors in terms of both accuracy and forecast bias. Code is available here: https://github.com/boreshkinai/nbeats-midterm." @default.
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• W3159352807 date "2021-07-01" @default.
• W3159352807 modified "2023-10-18" @default.
• W3159352807 title "N-BEATS neural network for mid-term electricity load forecasting" @default.
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• W3159352807 doi "https://doi.org/10.1016/j.apenergy.2021.116918" @default.
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