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基于大数据分析与LASSO分位数回归的电力负荷概率密度预测方法_硕士论文

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电力负荷预测对电力系统的调度,规划和生产计划起到了重要的作用。准确 的负荷预测为电力系统的平稳运行保驾护航。在实际生活中,电力负荷通常会受 到多种外界因素的影响,比如社会,经济,环境以及包含水电,风电及太阳能的 可再生能源的影响。这些潜在外界影响因子及随机因素的干扰往往使得电力负荷 预测变得越来越复杂。尤其是在智能电网中,基于大数据环境下,如何从复杂的 外界因素中高效提取出有价值的信息成为了电力负荷预测的关键。 为了解决电力负荷预测中的高维数据问题,降低预测过程中的不确定性,本 文提出了基于 LASSO 分位数回归(LASSO-QR)的概率密度预测方法和基于 LASSO 神经网络分位数回归(LASSO-QRNN)的概率密度预测方法。首先,利 用LASSO回归算法从电力负荷预测潜在的影响因子中筛选出重要的解释变量,建 立 LASSO-QR 模型和 LASSO-QRNN 模型。接着结合核密度估计方法,进行电力 负荷概率密度预测。预测结果不仅可以得到未来负荷完整的概率密度曲线,而且 也得到了未来负荷较准确的预测值和波动范围。为验证本文提出方法的有效性, 从统计学的角度出发,本文对概率密度曲线的概率均值、中位数和众数上得到的 点预测结果进行评估,同时运用相关预测区间评价准则对预测区间进行评价。 本文主要提出基于LASSO-QR的概率密度预测方法进行短期和中期电力负荷 预测,提出基于LASSO-QRNN的概率密度预测方法进行电力消费预测。在预测过 程中,均充分考虑外界影响因素挑选出重要的影响因子来构建合适的数学模型。 本文采用五个案例进行仿真实验,包含:加拿大安大略省冬季和夏季短期电力负 荷预测,中国东部某副省级市的中期电力负荷预测、中国广东省电力消费预测及 美国加利福尼亚州电力消费预测。通过与其他先进方法的对比实验,进一步表明 本文提出的概率密度预测方法可以显著降低预测过程中的不确定性,提高电力负 荷预测的准确性。在科学性的基础上,本文提出的方法不仅能满足电力系统决策 人员的要求,避免重大的经济损失,而且也为解决大数据环境下负荷预测问题寻 找到一个有效途径,具有重要的实际意义。 关键词:LASSO 分位数回归;LASSO 神经网络分位数回归;概率密度预测;高 维数据分析;电力负荷III ABSTRACT Power load forecasting plays an important role in power system scheduling, planning and production planning. Accurate load forecasting helps us ensure safe and stable operation of the power system. The power load forecasting is a challenging task, because the predictive accuracy is easily affected by multiple external factors, such as society, economics, environment, as well as the renewable energy, including hydro power, wind power and solar power. These potential external influence factors and the interference of random factors often make power load forecasting complicated increasingly. Particularly, in the smart grid with large amount of data, how to extract valuable information of those external factors timely is the key to the success of power load forecasting. . In order to solve the high-dimensional data problem in power load forecasting, the paper presents a probability density forecasting method based on LASSO quantile regression (LASSO-QR) and a probability density forecasting method based on LASSO neural network quantile regression (LASSO-QRNN) to reduce the uncertainty in the prediction process. Firstly, the LASSO regression algorithm is used to screen out the important explanatory variables from the potential impact factors of power load forecasting, and the LASSO-QR model and LASSO-QRNN model are established.Then, combined with the kernel density estimation method, the power load probability density forecastingisperformed.Thepredictionresultscan obtainnotonlytheprobabilitydensity curve of the future load intact, but also the predicted value and fluctuation range of the future load. For verifying the effectiveness of the proposed method, from a statistical point of view, this paper evaluates the point prediction results on the probability mean, median and mode of the probability density curve, and estimates the prediction interval using the relevant prediction interval evaluation criteria. In this paper, the probability density forecasting method based on LASSO-QR is proposed for short-term and medium-term load forecasting. Moreover, the probability densityforecasting method based on LASSO-QRNN is presented for power consumption forecasting. In the process of forecasting, the external influence factors are taken into account to select important influence factors for constructing a suitable mathematical model. This paper adopts five cases to carry out simulation experiments, including short- term power load forecasting in winter and summer in Ontario, Canada, medium-term power load forecasting in a sub-provincial city in eastern China, electricity consumption forecasting in Guangdong Province, China and electricity consumption forecasting inCalifornia, USA. Through comparison experiments with the state of the arts, it is further shown that the probability density forecasting method proposed in this paper can significantly reduce the uncertainty in the prediction process and improve the accuracy of power load forecasting. On the basis of science, the method proposed in this paper can not only meet the requirements of power system decision-makers to avoid major economic losses, but also find an effective way to solve the load forecasting problem in big data environment, which has important practical significance. KEYWORDS: LASSO quantile regression; LASSO quantile regression neural network; probability density forecasting; high dimensional data analysis; power loadV 目录 第一章 绪论.................................................................................................................... 1 1.1研究背景与意义..................................................................................................... 1 1.2国内外研究现状..................................................................................................... 2 1.2.1电力负荷预测研究现状 .................................................................................. 2 1.2.2概率性预测(概率性区间预测、概率密度预测) ...................................... 6 1.2.3基于大数据分析的预测方法研究 .................................................................. 7 1.3研究思路和研究方法............................................................................................. 8 1.3.1 研究思路.......................................................................................................... 8 1.3.2 研究方法.......................................................................................................... 9 1.4创新点及全文结构安排....................................................................................... 10 1.4.1 创新点............................................................................................................ 10 1.4.2 结构安排.........................................................................................................11 第二章 相关理论概述.................................................................................................. 13 2.1 LASSO回归......................................................................................................... 13 2.1.1模型表示 ........................................................................................................ 13 2.1.2模型求解及参数估计 .................................................................................... 14 2.2 分位数回归.......................................................................................................... 14 2.2.1模型表示 ........................................................................................................ 15 2.2.2 参数估计........................................................................................................ 15 2.3 神经网络分位数回归.......................................................................................... 16 第三章 考虑风电影响的短期电力负荷概率密度预测研究...................................... 18 3.1考虑风电因素的 LASSO 分位数回归模型 ........................................................ 18 3.2考虑风电因素的基于 LASSO 分位数回归的概率密度预测 ............................ 19 3.3算例分析............................................................................................................... 20 3.3.1 评价准则....................................................................................................... 20 3.3.2 案例描述........................................................................................................ 21 3.3.3 加拿大安大略省 2017年冬季案例分析 ...................................................... 24 3.3.4 加拿大安大略省 2017年夏季案例分析 ...................................................... 28 第四章 基于 LASSO 分位数回归的中期电力负荷概率密度预测............................ 32 4.1基于 LASSO 分位数回归模型 ............................................................................ 324.2基于 LASSO 分位数回归的中期电力负荷概率密度 ........................................ 33 4.3算例分析............................................................................................................... 33 4.3.1 算例描述....................................................................................................... 33 4.3.2 概率密度预测结果分析............................................................................... 34 第五章 基于 LASSO 神经网络分位数回归的电力消费概率密度预测.................... 39 5.1基于 LASSO 神经网络分位数回归模型 ............................................................ 39 5.2基于核密度估计的 LASSO-QRNN 概率密度预测方法.................................... 39 5.3算例分析............................................................................................................... 40 5.3.1 评价准则....................................................................................................... 40 5.3.2 中国广东省电力消费预测案例分析........................................................... 41 5.3.3 美国加利福尼亚电力消费预测案例分析................................................... 46 第六章 总结与展望...................................................................................................... 52 6.1主要研究工作及解决的实际问题....................................................................... 52 6.2研究工作不足及展望........................................................................................... 54