Titlebook: Application of Machine Learning and Deep Learning Methods to Power System Problems; Morteza Nazari-Heris,Somayeh Asadi,Milad Sadat-Moh Boo

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Introduction to Machine Learning Methods in Energy Engineering,r systems. On the other hand, ever-expanding energy consumption, development of industry and technology systems, and high penetration of solar and wind energies have made electricity networks operate in more complex and uncertain conditions. Therefore, analysis of traditional power and energy system

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A Survey of Recent Particle Swarm Optimization (PSO)-Based Clustering Approaches to Energy Efficienressure, etc.) of the target area and transmit the collected data to a central location. WSNs have been gaining an overwhelming interest for industrial applications due to their relatively low-cost and simple frameworks. In particular, they have also been recognized as a promising tool in power syst

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Clustering in Power Systems Using Innovative Machine Learning/Deep Learning Methods,uable but raw data have become available. The synchronized data, which are gathered throughout the system, has opened up new horizons for power system monitoring and control. Most importantly, they consist of hidden patterns which, if appropriately harnessed, could facilitate derivation of innovativ

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Voltage Stability Assessment in Power Grids Using Novel Machine Learning-Based Methods, collapse. In such a situation, a fast and accurate assessment of voltage stability is necessary to prevent large-scale blackouts. Machine learning techniques are widely applied in the voltage stability assessment according to their ability to train offline and predict results online. This paper pre