Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm
Wind Power Grid Connected Capacity Prediction Using LSSVM Optimized by the Bat Algorithm
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DOI：
10.3390/en81212428
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发表时间：
2015-12
期刊：
影响因子：
3.2
通讯作者：
Qunli Wu;Chenyang Peng
中科院分区：
文献类型：
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作者：
Qunli Wu;Chenyang Peng
鉴于风能的随机性，风电并网容量预测在应对供需平衡的挑战中起着至关重要的作用。准确的预测方法对于制定风电战略、电力调度和风电产业的可持续发展有着巨大的贡献。为了提高预测性能，提出了一种BAT算法(BA)-最小二乘支持向量机(LSSVM)混合模型。为了有效地选择LSSVM的输入，通过平稳性检验、协整检验和格兰杰因果检验检验了不同滞后时间对装机容量的影响，并利用偏自相关分析研究了并网容量之间的内在联系。为了验证最小二乘支持向量机的学习能力和泛化能力，对最小二乘支持向量机的参数进行了优化。综合运用多种模型充分性评价方法。研究结果表明，与其他单一或混合模型相比，该方法的精度提高可达20%左右。
Given the stochastic nature of wind, wind power grid-connected capacity prediction plays an essential role in coping with the challenge of balancing supply and demand. Accurate forecasting methods make enormous contribution to mapping wind power strategy, power dispatching and sustainable development of wind power industry. This study proposes a bat algorithm (BA)–least squares support vector machine (LSSVM) hybrid model to improve prediction performance. In order to select input of LSSVM effectively, Stationarity, Cointegration and Granger causality tests are conducted to examine the influence of installed capacity with different lags, and partial autocorrelation analysis is employed to investigate the inner relationship of grid-connected capacity. The parameters in LSSVM are optimized by BA to validate the learning ability and generalization of LSSVM. Multiple model sufficiency evaluation methods are utilized. The research results reveal that the accuracy improvement of the present approach can reach about 20% compared to other single or hybrid models.