基于神经网络和相似搜索技术的电力价格钉预测方法

电力市场中普遍存在“价格钉”,其特殊性给电价预测带来困难。通过对其特征和影响因素的分析,提出了一种基于神经网络和相似搜索技术的价格钉预测方法。首先,采用BP神经网络建立价格钉识别模型,对未来某一交易时段市场清算电价可能出现的区间进行预测,并提供相应的置信度;其次,对判别为价格钉的时段采用相似搜索技术进行二次预测。采用澳大利亚昆士兰州电力市场2004年至2005年全年的电价数据进行训练和预测分析,结果验证了该方法的有效性。

Electricity Market Price Spike Forecasting Based on Neural Network and Similarity searching Techniques

Due to the complexity and uncertainties in the power grid,electricity prices are highly volatile and normally carry with spikes,which may be tens or even hundreds of times higher than the normal price.Such electricity spikes are very difficult to be predicted.So far,the most research on electricity price forecast is based on the normal range electricity prices.Focusing on the price spike,this paper proposes a price interval identification model using neural network,which can generate forecasted price interval,level of spike and associated forecast confidence.Moreover,similarity searching technique was applied to further predict actual value of the price spike within the predicted interval.The model is tested with the Queensland electricity market data with promising results.