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• W4290717189 abstract "This paper aims to develop an evolutionary deep learning based hybrid data driven approach for short term load forecasting (STLF) in the context of Bangladesh. With the lapse of time, the power system is getting complex. Penetration of intermittent renewable energy (RE) into the grid, changing prosumer load pattern with the need of demand side management (DSM) has thrown a challenge for dynamic power system operation and control. Load forecasting plays a significant role in this dynamic operation and control. In addition, it directly affects the future planning of network expansion, unit commitment and economic energy mix for power market. Day ahead short-term forecasting is very crucial for day to day operation. As such, various conventional and modified methods have been used over time for short-term prediction. Nevertheless, the existing approaches like age old statistical methods, artificial intelligence (AI), machine learning (ML), deep learning (DL) techniques alone cannot provide effective accuracy all the time. Hence, an integrated genetic algorithm (GA)-bidirectional gated recurrent unit (Bi-GRU) hybrid data driven technique (GA-BiGRU) is proposed in this work. The developed method is validated in Bangladesh power system (BPS) network by providing day ahead forecasting of electrical load of the whole country. Besides, the performance of the prediction model is compared with some existing approaches such as long short-term memory network (LSTM), gated recurrent unit (GRU) and integrated genetic algorithm-gated recurrent unit (GA-GRU) in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The outcome gives an indication of better forecasting accuracy of proposed GA-BiGRU evolutionary DL technique compared to others." @default.
• W4290717189 created "2022-08-09" @default.
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• W4290717189 date "2022-01-01" @default.
• W4290717189 modified "2023-09-25" @default.
• W4290717189 title "A Data Driven Approach for Day Ahead Short Term Load Forecasting" @default.
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• W4290717189 doi "https://doi.org/10.1109/access.2022.3197609" @default.
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