Abstract
In this paper, enhanced Deep Learning (DL) method is implemented to resolve the accurate electricity load forecasting problem. Electricity load is a factor which plays major role in operations of Smart Grid (SM). For solving this problem, we propose a model which is based on preprocessing, selection and classification of historical data. Features are selected by Combine Feature Selection (CFS) using Decision Tree (DT) and Mutual Information (MI) techniques, and then CFS Convolutional Neural Network (CFSCNN) is used for forecasting of load. Our proposed scheme is also compared with other benchmark schemes. Simulation results show better efficiency and accuracy of proposed model for half hourly electricity load forecasting for one day, one week and one month ahead for the data obtained from ISO NE-CA electricity market.
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Khan, A.B.M., Khan, S., Aimal, S., Khan, M., Ruqia, B., Javaid, N. (2020). Half Hourly Electricity Load Forecasting Using Convolutional Neural Network. In: Barolli, L., Xhafa, F., Hussain, O. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2019. Advances in Intelligent Systems and Computing, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-22263-5_17
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DOI: https://doi.org/10.1007/978-3-030-22263-5_17
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