Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study

Andrei M. Tudose, Irina I. Picioroaga, Dorian O. Sidea, Constantin Bulac and Valentin A. Boicea
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Andrei M. Tudose: Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Irina I. Picioroaga: Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Dorian O. Sidea: Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Constantin Bulac: Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania
Valentin A. Boicea: Department of Electrical Power Systems, University “Politehnica” of Bucharest, 060042 Bucharest, Romania

Energies, 2021, vol. 14, issue 13, 1-19

Abstract: Short-term load forecasting (STLF) is fundamental for the proper operation of power systems, as it finds its use in various basic processes. Therefore, advanced calculation techniques are needed to obtain accurate results of the consumption prediction, taking into account the numerous exogenous factors that influence the results’ precision. The purpose of this study is to integrate, additionally to the conventional factors (weather, holidays, etc.), the current aspects regarding the global COVID-19 pandemic in solving the STLF problem, using a convolutional neural network (CNN)-based model. To evaluate and validate the impact of the new variables considered in the model, the simulations are conducted using publicly available data from the Romanian power system. A comparison study is further carried out to assess the performance of the proposed model, using the multiple linear regression method and load forecasting results provided by the Romanian Transmission System Operator (TSO). In this regard, the Mean Squared Error (MSE), the Mean Absolute Error (MAE), the Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) are used as evaluation indexes. The proposed methodology shows great potential, as the results reveal better error values compared to the TSO results, despite the limited historical data.

Keywords: convolutional neural networks; COVID-19; short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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