Abstract
Electricity load forecasting plays a vital role in improving the usage of energy through customers to make decisions efficiently. The accuracy of load prediction is a challenging task because of randomness and noise disturbance. An extreme deep learning model is applied in proposed system model to achieve better load prediction accuracy. The proposed model used to extract features by combining the mutual information (RF) and recursive feature elimination (RFE). Furthermore, extreme learning machine (ELM) and enhance CNN are used for load forecasting based on extracted features from MI and RFE. Additionally, to check the performance of our proposed scheme, we compared it with some benchmark schemes e.g. CNN, SVR and MLR. Simulation results reveal that our proposed approach outperformed in prediction performance.
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Faisal, H.M. et al. (2019). Prediction of Building Energy Consumption Using Enhance Convolutional Neural Network. In: Barolli, L., Takizawa, M., Xhafa, F., Enokido, T. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2019. Advances in Intelligent Systems and Computing, vol 927. Springer, Cham. https://doi.org/10.1007/978-3-030-15035-8_111
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DOI: https://doi.org/10.1007/978-3-030-15035-8_111
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