This paper creates a system for power load forecasting using support vector machine and ant colony optimization. The method of colony optimization is employed to process large amount of data and eliminate redundant information. The system mines the historical daily loading which has the same meteorological category as the forecasting day in order to compose data sequence with highly similar meteorological features. With this method, we reduced SVM training data and overcame the disadvantage of very large data and slow processing speed when constructing SVM model. This paper proposes a new feature selection mechanism based on ant colony optimization in an attempt to combat the aforemention difficulties. The method is then applied to find optimal feature subsets in the fuzzy-rough data reduction process. The present work is applied to complex systems monitoring, the ant colony optimization can mine the data more overall and accurate than the original fuzzy-rough method, an entropy-based feature selector, and a transformation-based reduction method, PCA. Comparing with single SVM and BP neural network in short-term load forecasting, this new method can achieve greater forecasting accuracy. It denotes that the SVM-learning system has advantage when the information preprocessing is based on data mining technology.

Hourly energy prices in a competitive electricity market are volatile. Forecast of energy price is key information to help producers and purchasers involved in electricity market to prepare their corresponding bidding strategies so as to maximize their profits. It is difficult to forecast all the hourly prices with only one model for different behaviors of different hourly prices. Neither will it get excellent results with 24 different models to forecast the 24 hourly prices respectively, for there are always not sufficient data to train the models, especially the peak price in summer. This paper proposes a novel technique to forecast day-ahead electricity prices based on Self-Organizing Map neural network (SOM) and Support Vector Machine (SVM) models. SOM is used to cluster the data automatically according to their similarity to resolve the problem of insufficient training data. SVM models for regression are built on the categories clustered by SOM separately. Parameters of the SVM models are chosen by Particle Swarm Optimization (PSO) algorithm automatically to avoid the arbitrary parameters decision of the tester, improving the forecasting accuracy. The comparison suggests that SOM-SVM-PSO has considerable value in forecasting day-ahead price in Pennsylvania-New Jersey-Maryland (PJM) market, especially forsummerpeak prices.

Middle-long forecasting of electric power load is crucial to electric investment, which is the guarantee of the healthy development of electric industry. In this paper, the particle swarm optimization (PSO) is used as a training algorithm to obtain the weights of the single forecasting method to form the combined forecasting method. Firstly, several forecasting methods are used to do middle-long power load forecasting. Then the upper forecasting methods are measured by several indices and the entropy method is used to form a comprehensive forecasting method evaluation index, following which the PSO is used to attain a combined forecasting method (PSOCF) with the best objective function value. We then obtain the final result by adding all the results of every single forecasting method. Taking actual load data of a power grid company in North China as a sample, the results show that PSOCF model improves the forecasting precision compared to the traditional models.

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models.

Anovelsupportvectorregression(SVR)optimizedbyanimprovedparticle swarm optimization (PSO) combined with simulated annealing algorithm (SA) was proposed. The optimization mechanism also combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVR kernel parameter setting. By incorporating with SA, the global searching capacity of the proposed model was enhanced. The improved SAPSO was used to optimize the parameters ofSVRand select the input features simultaneously. Based on the operational data provided by a regional power grid in north China, the method was used in short-term load forecasting (STLF). The experimental results showed the proposed approach can correctly select the discriminating input features and compared to the PSO-SVR and the traditional SVR, the average time of the proposed method in the experimental process reduced and the forecasting accuracy increased respectively. So, the improved method is better than the other two models.

针对电力系统负荷特性,分析灰色模型GM（1，1） 的应用局限性，引入向量α改进灰色模型背景值序列的计算公式，从而构建了适应性更强的GM（1，1,α）模型。应用粒子群优化算法非线性全局寻优能力来求解最优α值，提出了基于粒子群优化算法的灰色模型PSOGM，并给出了电力负荷预测的应用实例。实例证明PSOGM模型具有较高的预测精度和较广的应用范围。

支持向量机方法已成功地应用在负荷预测领域，但它在训练数据时存在数据处理量太大、处理速度慢等缺点。为此提出了一种基于数据挖掘预处理的支持向量机预测系统，引用在处理大数据量、消除冗余信息等方面具有独特优势的数据挖掘技术,寻找与预测日同等气象类型的多个历史短期负荷，由此组成具有高度相似气象特征的数据序列，将此数据序列作为支持向量机的训练数据，可减少数据量，从而提高预测的速度和精度，克服支持向量机的上述缺点。将该系统应用于短期负荷预测中，与单纯的SVM方法和BP神经网络法相比,得到了较高的预测精度。

利用支持向量机（SVM）和遗传算法（GA）建立24个不同的混合模型来对夏季24点负荷进行滚动预测。通过追加最新的负荷和天气信息来更新混合模型的输入，滚动预测下一小时负荷。利用SVM 建立预测模型，利用GA自动选择SVM模型的参数。经过GA优化后的最终SVM模型用于滚动预测下一小时的负荷。研究实例表明，GA简化了SVM参数选择，优化了SVM模型；滚动预测效果要明显好于常规预测方法。

In order to accurately forecast the load of power system and enhance the stability of the power network, a novel unascertained mathematics based recurrent neural network (UMRNN) for power intelligence center (PIC) was created through three steps. First, by combining with the general project uncertain element transmission theory (GPUET), the basic definitions of stochastic, fuzzy, and grey uncertain elements were given based on the principal types of uncertain information. Second, a power dynamic alliance including four sectors: generation sector, transmission sector, distribution sector and customers was established. The key factors were amended according to the four transmission topologies of uncertain elements, thus the new factors entered the power Intelligence center as the input elements. Finally, in the intelligence handing background of PIC, by performing uncertain and recursive process to the input values of network, and combining unascertained mathematics, the novel load forecasting model was built. Three different approaches were put forward to forecast an eastern regional power grid load in China. The root mean square error (ERMS) demonstrates that the forecasting accuracy of the proposed model UMRNN is 3% higher than that of BP neural network (BPNN), and 5% higher than that of autoregressive integrated moving average (ARIMA). Besides, an example also shows that the average relative error of the first quarter of 2008 forecasted by UMRNN is only 2.59%, which has high precision.

提高预测精度需要准确把握负荷变化规律和环境因素影响，但目前的分析方法多存在依赖主观经验，且对因素影响分析不深入的问题。为此，提出一种基于经验模式分解（empirical mode decomposition，EMD）和因素影响的负荷分析新方法。利用EMD 的自适应性，自动地将目标负荷序列分解为若干个独立的内在模式，可克服依赖主观经验的缺点。再利用多个指标从不同方面分析它们的规律特性。通过各分量与各影响因素的相关分析，深入挖掘各因素对各分量基金项目：国家自然科学基金项目（70671039）；高等学校博士点专项基金项目（20040079008）。Project Supported by National Natural Science Foundation of China（70671039），的影响情况。归纳出构成负荷的不同成分，并详细论述其特性。实例研究说明该方法可很好地分析负荷特性及因素影响。