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Energies 2018, 11(3), 596; https://doi.org/10.3390/en11030596

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

1
Department of Computer, Payame Noor University, Tehran 19305, Iran
2
School of Agricultural Computational and Environmental Sciences, International Centre for Applied Climate Science, Institute of Agriculture and the Environment, University of Southern Queensland, Springfield 4300, Australia
3
Department of Mathematics, University of Padua, Padua, Italy
4
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam
5
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
*
Author to whom correspondence should be addressed.
Received: 3 February 2018 / Revised: 22 February 2018 / Accepted: 5 March 2018 / Published: 8 March 2018
(This article belongs to the Collection Smart Grid)
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Abstract

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management. View Full-Text
Keywords: smart grid; load forecasting; machine learning; computational intelligence (CI); demand-side management smart grid; load forecasting; machine learning; computational intelligence (CI); demand-side management
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Fallah, S.N.; Deo, R.C.; Shojafar, M.; Conti, M.; Shamshirband, S. Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. Energies 2018, 11, 596.

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