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Today's energy resources are closer to consumers due to sustainable energy and advanced technology. To that end, ensuring a precise prediction of energy consumption at the buildings' level is vital and significant to manage the consumed energy efficiently using a robust predictive model. Growing concern about reducing the energy consumption of buildings makes it necessary to predict the future energy consumption precisely using an optimizable predictive model. Most of the previously proposed methods for energy consumption prediction are conventional prediction methods that are normally designed based on the developer's knowledge about the hyper-parameters. However, the time lag inputs and the network's hyper-parameters of learning methods need to be adjusted to have a more accurate prediction. This paper proposes a novel hybrid prediction approach based on the evolutionary deep learning (DL) method that is combining genetic algorithm with long short-term memory and optimizing its objective function with time window lags and the network's hidden neurons. The performance of the presented optimization predictive model is investigated using public building datasets of residential and commercial buildings for very short-term prediction, and the results indicate that the evolutionary DL models have better performance than conventional and regular prediction models.

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His research interests include power system analysis, renewable energy integration, energy management, power electronics, electrical vehicles, optimization, smart islands, smart cities and smart grids. He has supervised multiple M.Sc. and Ph.D. theses, worked on a number of technical projects, and published various papers and books. He has also joined the editorial board of some scientific journals and the steering committees of many international conferences.

His research interests include modeling smart buildings, smart grids, smart cities, and smart energy systems using artificial intelligence, machine learning, deep learning, and quantum computing.