A pragmatic investigation of energy consumption and utilization models in the urban sector using predictive intelligence approaches

Mohapatra S. K., Mishra S., Tripathy H. K., Bhoi A. K., Barsocchi P.

Energy consumption  Energy Engineering and Power Technology  Control and Optimization  Prediction  accuracy  Deep learning  Sustainability and the Environment  Electrical and Electronic Engineering  T  Technology  Machine learning  Engineering (miscellaneous)  Renewable Energy  Computational intelligence  Energy (miscellaneous) 

Energy consumption is a crucial domain in energy system management. Recently, it was observed that there has been a rapid rise in the consumption of energy throughout the world. Thus, almost every nation devises its strategies and models to limit energy usage in various areas, ranging from large buildings to industrial firms and vehicles. With technological advancements, computational intelligence models have been successfully contributing to the prediction of the consumption of energy. Machine learning and deep learning-based models enhance the precision and robustness compared to traditional approaches, making it more reliable. This article performs a review analysis of the various computational intelligence approaches currently being utilized to predict energy consumption. An extensive survey procedure is conducted and presented in this study, and relevant works are discussed. Different criteria are considered during the aggregation of the relevant studies relating to the work. The author's perspective, future trends and various novel approaches are also presented as a part of the discussion. This article thereby lays a foundation stone for further research works to be undertaken for energy prediction.

Source: Energies (Basel) 14 (2021). doi:10.3390/en14133900

Publisher: Molecular Diversity Preservation International, Basel

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