Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study
| Autor: | Irina I. Picioroaga, Andrei M. Tudose, Valentin A. Boicea, Dorian O. Sidea, Constantin Bulac |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
Control and Optimization Mean squared error Coronavirus disease 2019 (COVID-19) Computer science 020209 energy Energy Engineering and Power Technology Context (language use) short-term load forecasting 02 engineering and technology Convolutional neural network Electric power system Statistics Linear regression convolutional neural networks 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) Renewable Energy Sustainability and the Environment 020208 electrical & electronic engineering COVID-19 Term (time) Mean absolute percentage error Energy (miscellaneous) |
| Zdroj: | Energies, Vol 14, Iss 4046, p 4046 (2021) Energies; Volume 14; Issue 13; Pages: 4046 |
| ISSN: | 1996-1073 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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