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A Novel Accurate and Fast Converging Deep Learning-Based Model for Electrical Energy Consumption Forecasting in a Smart Grid^{†}

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Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad Campus 44000, Pakistan

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Department of Electrical Engineering, University of Engineering and Technology, Mardan 23200, Pakistan

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Department of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan

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Department of Electrical Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan

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Center of Excellence in Information Assurance, King Saud University, Riyadh 11653, Saudi Arabia

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Author to whom correspondence should be addressed.

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This paper is an extended version of our paper published in 13th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2019), Sydney, NSW, Australia, 3–5 July 2019; pp. 49–62. Advances in Intelligent Systems and Computing, volume 994. Springer, Cham.

Received: 18 March 2020 / Revised: 9 April 2020 / Accepted: 10 April 2020 / Published: 3 May 2020

(This article belongs to the Special Issue Energy Efficiency in Smart Homes and Grids)

Energy consumption forecasting is of prime importance for the restructured environment of energy management in the electricity market. Accurate energy consumption forecasting is essential for efficient energy management in the smart grid (SG); however, the energy consumption pattern is non-linear with a high level of uncertainty and volatility. Forecasting such complex patterns requires accurate and fast forecasting models. In this paper, a novel hybrid electrical energy consumption forecasting model is proposed based on a deep learning model known as factored conditional restricted Boltzmann machine (FCRBM). The deep learning-based FCRBM model uses a rectified linear unit (ReLU) activation function and a multivariate autoregressive technique for the network training. The proposed model predicts future electrical energy consumption for efficient energy management in the SG. The proposed model is a novel hybrid model comprising four modules: (i) data processing and features selection module, (ii) deep learning-based FCRBM forecasting module, (iii) genetic wind driven optimization (GWDO) algorithm-based optimization module, and (iv) utilization module. The proposed hybrid model, called FS-FCRBM-GWDO, is tested and evaluated on real power grid data of USA in terms of four performance metrics: mean absolute percentage deviation (MAPD), variance, correlation coefficient, and convergence rate. Simulation results validate that the proposed hybrid FS-FCRBM-GWDO model has superior performance than existing models such as accurate fast converging short-term load forecasting (AFC-STLF) model, mutual information-modified enhanced differential evolution algorithm-artificial neural network (MI-mEDE-ANN)-based model, features selection-ANN (FS-ANN)-based model, and Bi-level model, in terms of forecast accuracy and convergence rate.