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Mathematical Problems in Engineering
Volume 2017, Article ID 8192368, 8 pages
https://doi.org/10.1155/2017/8192368
Research Article

Reinforcement Learning Based Novel Adaptive Learning Framework for Smart Grid Prediction

Tian Li,1,2 Yongqian Li,1,2 and Baogang Li1,2

1Department of Computer Science, North China Electric Power University, Baoding, Hebei 071000, China
2Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, Hebei 071000, China

Correspondence should be addressed to Tian Li; moc.621@naitilupecn

Received 15 December 2016; Revised 25 February 2017; Accepted 23 March 2017; Published 18 April 2017

Academic Editor: Hung-Yuan Chung

Copyright © 2017 Tian Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Smart grid is a potential infrastructure to supply electricity demand for end users in a safe and reliable manner. With the rapid increase of the share of renewable energy and controllable loads in smart grid, the operation uncertainty of smart grid has increased briskly during recent years. The forecast is responsible for the safety and economic operation of the smart grid. However, most existing forecast methods cannot account for the smart grid due to the disabilities to adapt to the varying operational conditions. In this paper, reinforcement learning is firstly exploited to develop an online learning framework for the smart grid. With the capability of multitime scale resolution, wavelet neural network has been adopted in the online learning framework to yield reinforcement learning and wavelet neural network (RLWNN) based adaptive learning scheme. The simulations on two typical prediction problems in smart grid, including wind power prediction and load forecast, validate the effectiveness and the scalability of the proposed RLWNN based learning framework and algorithm.