组合预测法在中长期电力负荷预测中的研究与应用

电力系统中长期负荷预测是电力系统规划和运行研究的重要内容[1],它是保证电力系统可靠和经济运行的前提,同时也是电网规划建设的依据和基础。线性回归模型、指数平滑模型、灰色模型是常用的单模型中长期负荷预测方法,组合预测法是运用这三种方法的组合,通过运用最优权重系数方法来确定各个模型的权重,使预测的精度更高,误差更小。     

电力系统电优组合预测电力负荷的方法

介绍了趋势分析法、回归分析法、指数平滑法、单耗法、灰色模型法、负荷密度法和弹性系数法等电力负荷预测的方法,并以预测牡丹江市全社会年用电量为实例,在适用条件、数据形式、计算难度和适用时间等方面对几种预测方法进行分析、比较。得出结论:回归分析法、趋势分析法适用于大样本,且过去、现在和未来发展模式均一致的预测,灰色模型法适用于贫信息条件下的预测;灰色系统理论采用生成数序列建模,回归分析法、趋势分析法采用原始数据建模,指数平滑法是通过对原始数据进行指数加权组合直接预测未来值的;回归分析法和趋势分析法的计算相对简单;单耗法、指数平滑法、灰色模型法较适宜近期预测,回归法、趋势分析法和改进型灰色模型较适于中、长期预测。     

中长期负荷预测方法探究及其系统研发

中长期负荷预测是电力系统运行和规划的前提和基础,也是电力系统可靠运行的根本。其预测的准确与否直接关系到投资、网络布局和运行的合理性。因此对电力系统进行中长期负荷预测以及其预测方法的研究尤为重要。文章介绍了负荷预测对电力系统的重要性以及影响负荷的主要因素,综述了负荷总量和分布的预测方法,如灰色预测方法、组合预测方法和空间负荷预测方法,最后对计算机结合各种预测方法的预测系统进行研发。     

中长期电力负荷变权重组合预测模型应用分析

针对电力负荷变权组合预测中权重难以确定,本文引入了模糊变权重组合预测模型,该模型利用预测误差绝对平均值、预测误差绝对累加来确定各单项预测模型的权重,引入“近大远小”策略、冗余检验原则,实现组合预测模型筛选。应用结果分析表明,变权重组合预测模型在中长期电力负荷预测结果的精度较高,实用性较好。     

基于预测有效度成分数据处理方法组合预测

为提高成分数据时序预测准确性,提出一种以二阶预测有效性作标准的多种数据处理方法的组合预测。选择成分数据的多种数据转化方法,将有约束时序用对数比,中心对数,超球面变换方法转换成无约束时序后,利用ARIMA—ANN模型对转换后无约束时序预测,对结果做反变换,恢复为成分数据得单项预测结果。最后对得到的单项预测结果进行基于二阶预测有效度的加权几何平均组合,得到相对最优的组合预测结果。     

季度宏观经济的一种组合预测法

本文提出了一种季度宏观经济的组合预测法。该方法是在动态模型,即在结构模型的基础上进行预测,然后以最优组合预测法对单变量时间序列截面数据进行预测,对两种方法预测出的结果进行平均。研究表明,这种组合预测法具有良好的预测效果,从而为季度宏观经济预测提供了一种新的有效的方法。     

电力系统负荷预测方法的探讨

负荷预测是指在充分考虑一些重要的系统运行特点、增容决策、自然条件和社会影响的条件下,研究出一套可以用系统处理过去与未来负荷的数学方法,在满足一定精度要求的前提下,确定某特定时刻的负荷值。文章介绍几种较为适用的预测方法。     

电力系统负荷预测方法分析及应用

文章简要介绍了电力系统负荷预测的基本原理,分析了几种主要的电力系统负荷预测的方法,最后结合实际举例说明了几种预测方法的应用。     

负荷预测模型的建立及基于回归分析法的负荷预测

文章介绍了几种不同负荷特性的定义及预测模型。根据负荷预测的基本步骤,结合某地区电网历史数据实际情况分析研究,限于同一季节中,温差变化不大时,在超短期预测中选择出一种一元线性预测回归模型。应用于算例分析,最终得到预测结果,精度较高,说明了该方法的实用性和有效性。     

电力系统中长期负荷预测方法的研究与程序设计

文章探讨了电力系统负荷的组成、特点,在分析比较常用的预测方法优缺点的基础之上,采用了灰色预测法与回归法相结合的方法建立了中长期负荷预测模型,把负荷预测工作分为2个部分:即用灰色预测法进行相关因素的预测和用回归法进行负荷预测。该模型充分利用了灰色预测法要求负荷数据少、不考虑分布规律、不考虑变化趋势、运算方便、易于检验等优点及回归法能够考虑到负荷所受的多种因素的特点,模型参数估计技术比较成熟,预测过程简单。     

The impact of institutional change on forecast accuracy: A case study of budget forecasting in Washington State

This paper explores the relationship between institutional change and forecast accuracy via an analysis of the entitlement caseload forecasting process in Washington State. This research extends the politics of forecasting literature beyond the current area of government revenue forecasting to include expenditure forecasting and introduces an in-depth longitudinal study to the existing set of cross-sectional studies. Employing a fixed-effects model and ordinary least squares regression analysis, this paper concludes that the establishment of an independent forecasting agency and subsequent formation of technical workgroups improve forecast accuracy. Additionally, this study finds that more frequent forecast revisions and structured domain knowledge improve forecast accuracy.     

组合预测模型在武汉港集装箱吞吐量预测中的应用

为使港口集装箱吞吐量预测的误差更小,精度更高,提出运用弹性系数法、灰色模型法、三次指数平滑法的组合预测模型,预测了武汉港未来特征年的集装箱吞吐量,研究结果表明,组合模型相比单一预测方法能够降低误差、提高精度,预测结果更加理想。     

电力系统短期负荷混沌预测法

电力系统短期负荷预测,在日常工作中具有十分重要的意义,它是保证电力系统的安全、经济运行的基础。文章简要对短期负荷预测的研究方法进行介绍,详细分析了混沌理论预测方法,包括相空间重构等主要思想。另外,选择合适的综合预测模型才是提高预测精度的主要方法。     

Recent advances in intra-hour solar forecasting: A review of ground-based sky image methods

As the penetration of solar energy generation into power systems keeps rising, intra-hour solar forecasting (IHSF) is becoming increasingly important for the secure and economical operation of a power system. One major difficulty in providing very accurate IHSF emanates from rapid cloud changes in the sky. The ground-based sky image (GSI) provides the intuitive information of intra-hour cloud changes and has thus been widely utilized in studies on IHSF. This paper presents a systematic review of the state-of-the-art of ground-based sky image-based intra-hour solar forecasting (GSI-IHSF). To our knowledge, we first propose a generic framework of GSI-IHSF consisting of four modules, i.e., sky image acquisition, sky image preprocessing, cloud forecasting, and solar forecasting. Then, as for each module, this paper introduces its core function, shows the major challenges, briefly reviews several extensively used techniques, summarizing research trends. Finally, this paper offers a prospect of GSI-IHSF research, discusses recent advances that demonstrate the potential for a great improvement in forecast accuracy, pointing out some new requirements and challenges that should be further investigated in the future.     

基于组合模型的中长期电力负荷预测研究

本文研究了组合预测的模型,提高了预测的准确度。并对甘肃省2011-2020年全社会用电量做组合预测。     

A robust support vector regression model for electric load forecasting

Electric load forecasting is a crucial part of business operations in the energy industry. Various load forecasting methods and techniques have been proposed and tested. With growing concerns about cybersecurity and malicious data manipulations, an emerging topic is to develop robust load forecasting models. In this paper, we propose a robust support vector regression (SVR) model to forecast the electricity demand under data integrity attacks. We first introduce a weight function to calculate the relative importance of each observation in the load history. We then construct a weighted quadratic surface SVR model. Some theoretical properties of the proposed model are derived. Extensive computational experiments are based on the publicly available data from Global Energy Forecasting Competition 2012 and ISO New England. To imitate data integrity attacks, we have deliberately increased or decreased the historical load data. Finally, the computational results demonstrate better accuracy of the proposed robust model over other recently proposed robust models in the load forecasting literature.     

Combination of long term and short term forecasts, with application to tourism demand forecasting

Forecast combination is a well-established and well-tested approach for improving the forecasting accuracy. One beneficial strategy is to use constituent forecasts that have diverse information. In this paper we consider the idea of diversity being accomplished by using different time aggregations. For example, we could create a yearly time series from a monthly time series and produce forecasts for both, then combine the forecasts. These forecasts would each be tracking the dynamics of different time scales, and would therefore add diverse types of information. A comparison of several forecast combination methods, performed in the context of this setup, shows that this is indeed a beneficial strategy and generally provides a forecasting performance that is better than the performances of the individual forecasts that are combined.As a case study, we consider the problem of forecasting monthly tourism numbers for inbound tourism to Egypt. Specifically, we consider 33 individual source countries, as well as the aggregate. The novel combination strategy also produces a generally improved forecasting accuracy.     

考虑市场力的短期边际电价预测

电价波动较负荷波动剧烈,使得整个电价的预测精度降低。造成这种价格波动的主要原因是由于在电力市场中,发电商拥有的市场力具有能够支配电价上下波动的能力,使得电价的变化更加难以预测。因此市场力在电价预测中是必须考虑的重要因素之一。提出将市场供需比指标作为电价预测的一个输入量,将其引入到预测模型中作为影响电价的因素,使预测精度得到提高。     

Benchmarking robustness of load forecasting models under data integrity attacks

As the internet’s footprint continues to expand, cybersecurity is becoming a major concern for both governments and the private sector. One such cybersecurity issue relates to data integrity attacks. This paper focuses on the power industry, where the forecasting processes rely heavily on the quality of the data. Data integrity attacks are expected to harm the performances of forecasting systems, which will have a major impact on both the financial bottom line of power companies and the resilience of power grids. This paper reveals the effect of data integrity attacks on the accuracy of four representative load forecasting models (multiple linear regression, support vector regression, artificial neural networks, and fuzzy interaction regression). We begin by simulating some data integrity attacks through the random injection of some multipliers that follow a normal or uniform distribution into the load series. Then, the four aforementioned load forecasting models are used to generate one-year-ahead ex post point forecasts in order to provide a comparison of their forecast errors. The results show that the support vector regression model is most robust, followed closely by the multiple linear regression model, while the fuzzy interaction regression model is the least robust of the four. Nevertheless, all four models fail to provide satisfying forecasts when the scale of the data integrity attacks becomes large. This presents a serious challenge to both load forecasters and the broader forecasting community: the generation of accurate forecasts under data integrity attacks. We construct our case study using the publicly-available data from Global Energy Forecasting Competition 2012. At the end, we also offer an overview of potential research topics for future studies.     

Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match

This paper describes the methods used by Team Cassandra, a joint effort between IBM Research Australia and the University of Melbourne, in the GEFCom2017 load forecasting competition. An important first phase in the forecasting effort involved a deep exploration of the underlying dataset. Several data visualisation techniques were applied to help us better understand the nature and size of gaps, outliers, the relationships between different entities in the dataset, and the relevance of custom date ranges. Improved, cleaned data were then used to train multiple probabilistic forecasting models. These included a number of standard and well-known approaches, as well as a neural-network based quantile forecast model that was developed specifically for this dataset. Finally, model selection and forecast combination were used to choose a custom forecasting model for every entity in the dataset.