A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches

Sunil Kumar Mohapatra, Sushruta Mishra, Hrudaya Kumar Tripathy, Akash Kumar Bhoi and Paolo Barsocchi
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Sunil Kumar Mohapatra: Kalinga Institute of Industrial Technology, School of Computer Engineering, Bhubaneswar 751024, India
Sushruta Mishra: Kalinga Institute of Industrial Technology, School of Computer Engineering, Bhubaneswar 751024, India
Hrudaya Kumar Tripathy: Kalinga Institute of Industrial Technology, School of Computer Engineering, Bhubaneswar 751024, India
Akash Kumar Bhoi: Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, India
Paolo Barsocchi: Institute of Information Science and Technologies, National Research Council, 56124 Pisa, Italy

Energies, 2021, vol. 14, issue 13, 1-28

Abstract: 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.

Keywords: energy consumption; prediction; computational intelligence; machine learning; deep learning; accuracy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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