Big data analytics based short term electricity load forecasting model for residential buildings in smart grids

Khan, Inam Ullah and Javaid, N and Taylor, C. James and Gamage, K.A.A. and Ma, Xiandong (2020) Big data analytics based short term electricity load forecasting model for residential buildings in smart grids. In: IEEE INFOCOM 2020 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) :. IEEE, pp. 544-549. ISBN 9781728186955

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Abstract

Electricity load forecasting has always been a significant part of the smart grid. It ensures sustainability and helps utilities to take cost-efficient measures for power system planning and operation. Conventional methods for load forecasting cannot handle huge data that has a nonlinear relationship with load power. Hence an integrated approach is needed that adopts a coordinating procedure between different modules of electricity load forecasting. We develop a novel electricity load forecasting architecture that integrates three modules, namely data selection, extraction, and classification into a single model. First, essential features are selected with the help of random forest and recursive feature elimination methods. This helps reduce feature redundancy and hence computational overhead for the next two modules. Second, dimensionality reduction is realized with the help of a t-stochastic neighbourhood embedding algorithm for the best feature extraction. Finally, the electricity load is forecasted with the help of a deep neural network (DNN). To improve the learning trend and computational efficiency, we employ a grid search algorithm for tuning the critical parameters of the DNN. Simulation results confirm that the proposed model achieves higher accuracy when compared to the standard DNN.