Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these...Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.展开更多
基金support from the Qatar National Research Fund through grant AICC05-0508-230001(Solar Trade(ST):An Equitable and Efficient Blockchain-Enabled Renewable Energy Ecosystem-“Oppor-tunities for Fintech to Scale up Green Finance for Clean Energy”)and from Qatar Environment and Energy Research Institute is gratefully acknowledged.
文摘Efficient energy management and grid stability strongly rely on accurate Short-Term Load Forecasting(STLF).Existing forecasting models,unfortunately,are often inaccurate and computationally demanding.To overcome these challenges,a novel hybrid model,combining both linear regression and machine learning techniques,is proposed in this study.The hybrid model,MLR-LSTM-FFNN,captures both temporal and non-linear de-pendencies in load data by integrating multi-linear regression(MLR)with long short-term memory(LSTM)networks and feed-forward neural networks(FFNN).Using datasets from Qatar,with 5 min,15 min,30 min,and 1 h time intervals and from Panama City with a 1 h interval,experiments were conducted to thoroughly test the robustness of the model.The results showed that the MLR-LSTM-FFNN hybrid model outperformed the baseline and state-of-the-art hybrid models for each of the datasets,in terms of lower RMSE,MAE,and MAPE values along with a faster training time.This superior performance across different datasets underscores the model’s scal-ability and reliability as an STLF approach,providing a practical solution to energy demand prediction tasks.The improvement in short-term forecasting accuracy provides utilities with a practical tool to optimize demand-side management,reduce operational costs,and enhance grid reliability.
