摘要
针对国内外工业用电采用基础电费和实际用电费用两部制电价的情况,研究针对拥有多台大功率电弧炉的高能耗冶金企业,通过对负荷进行均衡调节,降低最大负荷,从而降低基本电费的用户需求侧能量控制系统。短期负荷预测(Short Time Load Forecasting,STLF)算法是课题的核心研究内容。
本文主要完成如下几个方面的工作:
对目前国内外同类技术研究动态进行分析,对主流短期负荷预测算法原理、方法和特点及存在的问题进行探讨。说明本研究工作的实际背景、必要性和重要意义。
针对拥有多台大功率电弧炉供电系统负荷波动大、负荷容量难以选取的问题,独创性地提出一种基于阈交理论的负荷分析、计算新算法。该算法利用负荷中超阈值数据,采用方差分析方法构造一个阈值能量函数,获得阈值选取的依据。推导出穿越强度的计算公式,利用供电系统瞬时功率对阈值的穿越强度考察已选阈值的合理性。
依据历史负荷数据,给出GM(1,1)模型最优原始数据长度的确定方法,利用残差修正、等维新息等方法对预测结果进行修正。针对灰色理论、重建相空间G.P算法和人工神经网络各自特点,独立提出一种将上述算法模型相结合的短期负荷预测算法(G-G-NN)。该算法利用灰色预测的累加生成和重建相空间的G.P算法对原始时间序列进行变换,生成规律性较强的时间序列相空间,而后利用神经网络模型进行预测。获得比使用单一神经网络模型更高的预测精度和更好的实时性。
针对所研究系统短期负荷序列既有波动性又有特殊周期性的特点,利用小波良好的时频分析特性,将不同频率混合信号分解成不同频带上的信号,在各个尺度空间上利用不同的神经网络进行预测,而后进行重构完成预测。对利用不同小波函数进行预测的效果进行了比较和讨论,实际算例表明该算法可进一步提高负荷预测的精度。
提出一种联合数据挖掘与支持向量机的短期负荷预测算法,该算法利用数据挖掘中聚类算法对原始数据进行初期处理,将海量输入进行压缩,取其聚类中心作为支持向量机预测模型的输入特征,而后利用交叉验证判别法选择SVM的最优核函数,最终完成短期负荷预测。实际算例表明,该方法可有效地克服数据有限性、不完整性及影响因素复杂性等对预测结果的影响,具有较大的实际应用价值。
完成钢厂电能控制软件的研制和调试工作,利用Visual C++编程语言编写相关软件,形成可视化的人机交互式界面,实现对钢厂负荷的预测、控制和综合管理软件。
Considering the situation that electrical cost of industry at home and abroad includes two parts (basic cost and actual cost), an energy-saving electricity-consumption-control system is proposed for metallurgical enterprises with many high-power electric arc furnaces, which consume a great deal of energy, to reduce their basic electrical cost by evenly regulating the power and decreasing the maximum load. Efficient short-term load forecasting algorithm is the core issue of this study.
Following achievements have been obtained:
Detailed analysis to current similar technology at home and abroad is carried out. The main short-term load forecasting algorithms and their principles, methods and characteristics are discussed in depth. Actual background, necessity and significance of this study are explained.
Considering the difficulties of selecting the electric power capacity of high-power electric arc furnaces resulted by drastic power fluctuation, a new arithmetic based on the threshold theory is presented originally. The energy function of threshold value is constructed by the method of variance analysis, and the base for selecting threshold value is obtained. The formulas for calculating the crossing intensity are deducted. The rationality of the selected threshold is verified by the crossing intensity of the instant power of power-supply system to the threshold.
Based on historical load data, the method is put forward for determining the optimal data length of the grey model GM(1,1), and the forecasting results are amended by novel methods such as residual revision and filling innovation in proper order. The G-G-NN algorithm is proposed by combining the characteristics of grey theory, reconstruction-phase-space GP algorithm and artificial neural network (NN). Using grey theory and G.P algorithm, this new algorithm convert the original time series into the time series phase space with strong orderliness, and then the load is predicted by NN. The predicted results are of higher precision and better real-time than neural network.
Aiming at the fluctuation and periodicity of the load series, the new method based on the wavelet excellent characteristics to analysis time and frequency is suggested, with which the signal mixed with different frequencies is decomposed into signals in different frequency bands, and different neural networks are used to forecast the data in different scale space, then the forecasting results are obtained by reconstructed. Different wavelet functions are compared and discussed and it is proved by actual example that the more accurate results are forecasted by this method.
Another short-term load forecasting algorithm combining data mining algorithm and support-vector-machine method is provided. Firstly, the original data are preliminarily operated using clustering algorithm of data mining, the mass input data being compressed. Secondly, the clustering centre is chosen as the input characteristics of the support-vector-machine model, and the optimum core function is selected by cross-validating discrimination. Finally, the short-term load forecasting is finished. Actual examples show that the influences of the limitation and integrality of historical data and the complexity of factors on the forecasted results are minimized. This method has higher practical application value.
According these achievements, the electricity-consumption-control software for metallurgical enterprises is designed and debugged by using Visual C++, then form the visual human-computer interactive interface, the forecasting, controlling and comprehensive management of the power load for steel-making plants being realized.
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