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Electricity, the most important form of energy and an indispensable resource, primarily for commercial and residential smart buildings, faces challenges requiring its hyper efficient consumption and production. Therefore, accurate energy consumption predictions are required in order to manage and optimize the energy consumption of smart buildings. Many studies have taken advantage of the power and robustness of neural networks (NN) when it comes to accurate predictions. A few studies have also used the particle swarm optimization (PSO) algorithm along with NNs to enhance and optimize the predictions. In this work, we study prediction learning using PSO-based neural networks (PSO-NN) and propose modifications in order to increase prediction accuracy. Our proposed modifications are re-generation based PSO-NN (R-PSO-NN) and velocity boost-based PSO-NN (VB-PSO-NN). The performance metrics used are: prediction accuracy, number of particles used, and number of epochs required. We compare the results of NN, PSO-NN, R-PSO-NN and VB-PSO-NN based on the performance metrics.

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Prediction-Learning Algorithm for Efficient Energy Consumption in Smart Buildings Based on Particle Regeneration and Velocity Boost in Particle Swarm Optimization Neural Networks