Short-Term Load Interval Prediction Using a Deep Belief Network
AbstractIn load predication, point-based forecasting methods have been widely applied. However, uncertainties arising in load predication bring significant challenges for such methods. This therefore drives the development of new methods amongst which interval predication is one of the most effective. In this study, a deep belief network-based lower–upper bound estimation (LUBE) approach is proposed, and a genetic algorithm is applied to reinforce the search ability of the LUBE method, instead of simulated an annealing algorithm. The approach is applied to the short-term load prediction on some realistic electricity load data. To demonstrate the effectiveness and efficiency of the proposed method, it is compared with three state-of-the-art methods. Experimental results show that the proposed approach can significantly improve the predication accuracy. View Full-Text
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Zhang, X.; Shu, Z.; Wang, R.; Zhang, T.; Zha, Y. Short-Term Load Interval Prediction Using a Deep Belief Network. Energies 2018, 11, 2744.
Zhang X, Shu Z, Wang R, Zhang T, Zha Y. Short-Term Load Interval Prediction Using a Deep Belief Network. Energies. 2018; 11(10):2744.Chicago/Turabian Style
Zhang, Xiaoyu; Shu, Zhe; Wang, Rui; Zhang, Tao; Zha, Yabing. 2018. "Short-Term Load Interval Prediction Using a Deep Belief Network." Energies 11, no. 10: 2744.
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