Short-term electricity price forecasting is now crucial practice in deregulated electricity markets, as it forms the basis for maximizing the profits of the market participants. In this thesis, short-term electricity prices are forecast using three different predictor schemes, Artificial Neural Networks (ANNs), Support Vector Machine (SVM) and a hybrid scheme, respectively. ANNs are the very popular and successful tools for practical forecasting. In this thesis, a hidden-layered feed-forward neural network with back-propagation has been adopted for detailed comparison with other forecasting models. SVM is a newly developed technique that has many attractive features and good performance in terms of prediction. In order to overcome the limitations of individual forecasting models, a hybrid technique that combines Fuzzy-C-Means (FCM) clustering and SVM regression algorithms is proposed to forecast the half-hour electricity prices in the UK electricity markets. According to the value of their power prices, thousands of the training data are classified by the unsupervised learning method of FCM clustering. SVM regression model is then applied to each cluster by taking advantage of the aggregated data information, which reduces the noise for each training program. In order to demonstrate the predictive capability of the proposed model, ANNs and SVM models are presented and compared with the hybrid technique based on the same training and testing data sets in the case studies by using real electricity market data. The data was obtained upon request from APX Power UK for the year 2007. Mean Absolute Percentage Error (MAPE) is used to analyze the forecasting errors of different models and the results presented clearly show that the proposed hybrid technique considerably improves the electricity price forecasting.
展开▼
机译：在放松管制的电力市场中，短期电价预测现在已成为关键实践，因为它构成了最大化市场参与者利润的基础。在本文中，短期电价是使用三种不同的预测器方案进行预测的，分别是人工神经网络（ANN），支持向量机（SVM）和混合方案。人工神经网络是非常实用的实用预测工具。本文采用带有反向传播的隐层前馈神经网络与其他预测模型进行详细比较。 SVM是一项新开发的技术，在预测方面具有许多吸引人的功能和良好的性能。为了克服单个预测模型的局限性，提出了一种结合模糊C均值（FCM）聚类和SVM回归算法的混合技术来预测英国电力市场的半小时电价。根据电价的价值，通过FCM聚类的无监督学习方法对数千个训练数据进行分类。然后，通过利用聚合的数据信息将SVM回归模型应用于每个群集，从而减少每个训练程序的噪音。为了证明所提出模型的预测能力，在案例研究中，使用真实的电力市场数据，提出了人工神经网络和支持向量机模型，并与基于相同训练和测试数据集的混合技术进行了比较。该数据是应英国APX Power 2007的要求而获得的。使用平均绝对百分比误差（MAPE）来分析不同模型的预测误差，并且所呈现的结果清楚地表明，所提出的混合技术大大改善了电价的预测。
展开▼
