Abstract
Short-term electricity supply and demand forecasting using weather parameters including: temperature, wind speed, and solar radiations improve the operational efficiency and accuracy of power systems. There are many weather parameters which have influential affect on the supply and demand of electricity, but temperature, solar radiations, and wind speed are the most important parameters. Our proposed time series model is based on preprocessing, feature extraction, data preparation, and Enhanced Convolutional Neural Network referred as ECNN module for short-term weather parameters forecasting up to 6-h ahead. The proposed ECNN time series model is applied on 61 locations of United States, collected from National Solar Radiation Database (NSRDB). Model trained on 15-years data and validated on additional two-years out of sample data. Simulation result shows that our proposed model performs better than traditional benchmark models in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Relative Root Mean Square Error (RMSE%) performance metrics. Result shows that the proposed model is effective for short-term forecasting of temperature, solar radiations, and wind speed. Moreover, proposed model improves the accuracy and operational efficiency of power systems.
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Aslam, Z., Javaid, N., Adil, M., Ijaz, M.T., ur Rahman, A., Ahmed, M. (2020). An Enhanced Convolutional Neural Network Model Based on Weather Parameters for Short-Term Electricity Supply and Demand. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_3
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DOI: https://doi.org/10.1007/978-3-030-44041-1_3
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