Abstract
In 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.
Highlights
Load prediction plays an important role in the planning of power systems, building reliable power systems and so on
The proposed deep belief network (DBN)-based lower–upper bound estimation (LUBE) method was compared with three best-inclass prediction models, i.e., the Elman model [39], the nonlinear autoregressive exogenous (NARX)
The proposed DBN-based LUBE method was compared with three best-in-class
Summary
Load prediction plays an important role in the planning of power systems, building reliable power systems and so on. There has been a number of studies proposed for STLP These methods can be loosely categorized as point predication and interval prediction. The upper and lower bounds in interval predication can highly cover the fallen objectives, but they provide an accurate coverage probability as an indication, which obviously brings more quantitative information than point. The delta method adopts a nonlinear regression technique to enhance the generalization performance of the neural network (NN) models [15]. The method linearized the neural network model by a set of parameters generated by minimizing the sum of the squared error cost functions. Bayesian techniques are used to train neural networks, and they allow the predicted value to have a certain error range [17]. The low empirical coverage probability is the biggest drawback of this approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
