2nd Webinar from Webinar Series

Title:Big Data Analytics based Short Term Electricity Load Forecasting Model for Residential Buildings in Smart Grids
Speaker: Mr. Inamullah khan

Mr Inamullah Khan received his B.sc Electronics degree from FUUAST, Islamabad Pakistan and MS (Electrical Power) from COMSATS Islamabad Pakistan in 2009 and 2013 respectively. Since 2013 he has been with COMSATS department of electrical and computer engineering Lahore campus. In 2018 he started PhD from energy department Lancaster university UK. His research interests include supply side and demand side energy management in smart grid, renewable energy sources integration and big data analytics in smart grid


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, dimensional 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.
Index Terms—Big data, Electricity load forecasting, Feature engineering, Classification, Smart grid

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