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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.
Andrei Tudose; Irina Picioroaga; Dorian Sidea; Constantin Bulac; Valentin Boicea. Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study. Energies 2021, 14, 4046 .
AMA StyleAndrei Tudose, Irina Picioroaga, Dorian Sidea, Constantin Bulac, Valentin Boicea. Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study. Energies. 2021; 14 (13):4046.
Chicago/Turabian StyleAndrei Tudose; Irina Picioroaga; Dorian Sidea; Constantin Bulac; Valentin Boicea. 2021. "Short-Term Load Forecasting Using Convolutional Neural Networks in COVID-19 Context: The Romanian Case Study." Energies 14, no. 13: 4046.
The optimal reactive power dispatch (ORPD) problem represents a fundamental concern in the efficient and reliable operation of power systems, based on the proper coordination of numerous devices. Therefore, the ORPD calculation is an elaborate nonlinear optimization problem that requires highly performing computational algorithms to identify the optimal solution. In this paper, the potential of metaheuristic methods is explored for solving complex optimization problems specific to power systems. In this regard, an improved salp swarm algorithm is proposed to solve the ORPD problem for the IEEE-14 and IEEE-30 bus systems, by approaching the reactive power planning as both a single- and a multi- objective problem and aiming at minimizing the real power losses and the bus voltage deviations. Multiple comparison studies are conducted based on the obtained results to assess the proposed approach performance with respect to other state-of-the-art techniques. In all cases, the results demonstrate the potential of the developed method and reflect its effectiveness in solving challenging problems.
Andrei Tudose; Irina Picioroaga; Dorian Sidea; Constantin Bulac. Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm. Energies 2021, 14, 1222 .
AMA StyleAndrei Tudose, Irina Picioroaga, Dorian Sidea, Constantin Bulac. Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm. Energies. 2021; 14 (5):1222.
Chicago/Turabian StyleAndrei Tudose; Irina Picioroaga; Dorian Sidea; Constantin Bulac. 2021. "Solving Single- and Multi-Objective Optimal Reactive Power Dispatch Problems Using an Improved Salp Swarm Algorithm." Energies 14, no. 5: 1222.