江苏省电力消费量的影响因素及预测

为对影响江苏省电力消费量的因素进行分析,文章采用了单位根和协整分析技术,对江苏省三次产业与电力消费量之间的关系进行了实证分析,得到了有启示意义的结论;同时,还建立了VAR模型,对电力消费量进行了预测,其结果表明,该模型的精确度很高,具有较强的实用性。     

Using multivariate time series methods to estimate location and climate change effects on temperature readings employed in electricity demand simulation

Long‐term historical daily temperatures are used in electricity forecasting to simulate the probability distribution of future demand but can be affected by changes in recording site and climate. This paper presents a method of adjusting for the effect of these changes on daily maximum and minimum temperatures. The adjustment technique accommodates the autocorrelated and bivariate nature of the temperature data which has not previously been taken into account. The data are from Perth, Western Australia, the main electricity demand centre for the South‐West of Western Australia. The statistical modelling involves a multivariate extension of the univariate time series ‘interleaving method’, which allows fully efficient simultaneous estimation of the parameters of replicated Vector Autoregressive Moving Average processes. Temperatures at the most recent weather recording location in Perth are shown to be significantly lower compared to previous sites. There is also evidence of long‐term heating due to climate change especially for minimum temperatures.     

The Estimation of Short-Run Household Electricity Demand Using Pooled Aggregate Data

Short-run household electricity demand has been estimated with conditional demand models by a variety of authors using both aggregate data and disaggregate data. Disaggregate data are most desirable for estimating these models. However, in many cases, available disaggregate data may be inappropriate. Furthermore, disaggregate data may be unavailable altogether. In these cases, readily available aggregate data may be more appropriate. This article develops and evaluates an econometric technique to generate unbiased estimates of household electricity demand using such aggregate data.     

非线性定价有效吗

非线性定价存在于日常生活的方方面面,如阶梯电、水、气价格以及累进税率、通话套餐等。然而,价格信号的复杂性,使得消费者常常并未基于边际价格做出经济决策,关于非线性定价对于需求是否存在抑制的争论亦是此起彼伏。有鉴于此,本文基于Ito（2014）所提模型,利用广东省2010-2013年间各城市阶梯水价在时间维度和横截面维度的变异性以及微观住户月度用水消费数据,对消费者认知价格进行了甄别,并指出既往研究存在的识别问题。实证结果显示,住户当月消费基本是对上月平均价格而非边际价格或者期望边际价格做出反应,弹性约为-0.24。这说明,阶梯水价并未如设计初衷那样,通过跳跃性的价格激励机制降低住户用水需求,而是通过提高平均价格实现了对于住户用水的抑制。本文对于税率设计以及其他能源价格、通信套餐定价也有一定启发意义。     

A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction

In the framework of competitive electricity market, prices forecasting has become a real challenge for all market participants. However, forecasting is a rather complex task since electricity prices involve many features comparably with those in financial markets. Electricity markets are more unpredictable than other commodities referred to as extreme volatile. Therefore, the choice of the forecasting model has become even more important. In this paper, a new hybrid model is proposed. This model exploits the feature and strength of the auto-regressive fractionally integrated moving average model as well as least-squares support vector machine model. The expected prediction combination takes advantage of each model's strength or unique capability. The proposed model is examined by using data from the Nordpool electricity market. Empirical results showed that the proposed method has the best prediction accuracy compared to other methods.     

近20年来中国电力需求的行业与地域结构分析

采用行业结构和地域结构相结合的方法,分析了近20年来中国电力消费需求的变化,结果发现：随着国民经济的发展,电力消费量也呈同步增长态势;就四大行业的电力消耗来看,1986-2005年,第一产业的耗电量变化不大,城乡居民和第三产业耗电量都有所增长,第二产业是中国耗电量最大的行业,且年增长率较大;通过对中国31个省区人均电力消费量的分析,将其划分为6个等级,结果表明人均电力消费量与人均GDP呈正相关关系。在此基础上,以人口和人均GDP为变量,建立了中国电力发展的地域需求模型,发现城市化水平和经济发展程度是中国各省区电力需求的关键增长因素,而人口的弹性系数呈逐年下降趋势。     

强度效应、结构效应与中国电力消费之谜

 单位GDP用电量的变化是导致电力消费偏离经济增长的决定性因素。本文把国民经济细分为43个行业,运用对数平均指数法分解出2006-2009年电耗强度变化的结构效应与强度效应。结果表明：（1）总体上看,在电耗强度的变化中结构效应的比重不到30%,强度效应起主导作用,占70%以上。（2）各年间结构效应的绝对值都是负的,即产业结构的变化对于单位GDP用电量的下降具有积极的作用。（3）细分到具体行业,电力、钢铁、化工、有色金属和建材等高耗能产业的电耗强度和产值比重的变化对单位GDP用电量的变化起着主导作用。精确的分解分析不仅解释了中国电力消费之谜,对于节能减排政策也具有一定的启示作用：产业结构的调整是一个缓慢的循序渐进的过程,在短期内能耗强度下降的主导因素在于细分行业自身能耗强度下降,当前节能减排的重点和主要潜力在于提高细分行业的能源利用效率。     

Functional time series approach for forecasting very short-term electricity demand

This empirical paper presents a number of functional modelling and forecasting methods for predicting very short-term (such as minute-by-minute) electricity demand. The proposed functional methods slice a seasonal univariate time series (TS) into a TS of curves; reduce the dimensionality of curves by applying functional principal component analysis before using a univariate TS forecasting method and regression techniques. As data points in the daily electricity demand are sequentially observed, a forecast updating method can greatly improve the accuracy of point forecasts. Moreover, we present a non-parametric bootstrap approach to construct and update prediction intervals, and compare the point and interval forecast accuracy with some naive benchmark methods. The proposed methods are illustrated by the half-hourly electricity demand from Monday to Sunday in South Australia.     

A SARIMAX coupled modelling applied to individual load curves intraday forecasting

A dynamic coupled modelling is investigated to take temperature into account in the individual energy consumption forecasting. The objective is both to avoid the inherent complexity of exhaustive SARIMAX models and to take advantage of the usual linear relation between energy consumption and temperature for thermosensitive customers. We first recall some issues related to individual load curves forecasting. Then, we propose and study the properties of a dynamic coupled modelling taking temperature into account as an exogenous contribution and its application to the intraday prediction of energy consumption. Finally, these theoretical results are illustrated on a real individual load curve. The authors discuss the relevance of such an approach and anticipate that it could form a substantial alternative to the commonly used methods for energy consumption forecasting of individual customers.     

居民生活用电特征与影响机理

近年来我国家庭生活领域碳排放增长迅速,其中电力消费增长是导致家庭碳排放增加的重要原因。本文利用在上海地区开展的居民生活碳消费调查中的居住用电数据,分析了上海市常住居民家庭用电的特征和影响机理。样本中,上海户均年用电量为2184.6kWh,标准差为1398.5kWh,用电基尼系数为0.32,此外生活用电量呈现冬夏高、春秋低,且冬夏两季用电量的离散程度高于春秋两季的现象。回归模型显示上海居民生活用电受到人口规模、收入水平、居住面积、低碳态度和用能习惯的显著影响,且不同用电量家庭的用电影响因素种类和作用效果都存在变化：低用电家庭的生活用电受到人口规模、低碳态度和用能行为的影响,中等用电家庭的生活用电显著影响因素为人口规模、收入水平、低碳态度和用能行为,高用电家庭的生活用电受到人口规模、用能习惯和居住面积的影响;并且随着用电分布从低向高移动,各影响因素的作用效果或增高或降低,呈现不同的变化趋势。通过研究不同用电量家庭用电影响因素的变化,有利于更加深入地了解不同群体生活用电影响因素和完善生活领域电力消费的约束措施。     

SAR模型在省域和县域农民收入中的应用研究

 本文利用各省财政农业人均支出、农村人均用电量作为解释变量，建立了农民人均收入空间自回归(SAR)模型。模型数据分析表明：我国农民人均收入存在明显的空间自相关现象，它反映了省际农民收入存在聚集效应；农民人均收入对财政农业人均支出、农村人均用电量存在明显的空间依赖性；财政农业支出对农民增收产生积极的正面影响，农村用电量是衡量农民收入水平的重要有效指标。考虑到省际间差异可能太大，文章还对福建省67个县市进行了同样的问题研究，并得出了相似的结论。     

Statistical models for disaggregation and reaggregation of natural gas consumption data

In this paper, we present a unified framework for natural gas consumption modeling and forecasting. This consists of models of GAM class and their nonlinear extension, tailored for easy estimation, aggregation and treatment of the delayed relationship between temperature and consumption. Since the consumption data for households and small commercial customers are routinely available in many countries only as long-term sum meter readings, their disaggregation and possibly reaggregation to different time intervals is necessary for a variety of purposes. We show some examples of specific models based on the presented framework and then we demonstrate their use in practice, especially for the disaggregation and reaggregation tasks.     

Using Analysis of Variance and Factor Analysis for the Reduction of High Dimensional Variation in Time Series of Energy Consumption

Summary: In this paper the complexity of high dimensional data with cyclical variation is reduced using analysis of variance and factor analysis. It is shown that the prediction of a small number of main cyclical factors is more useful than forecasting all the time-points separately as it is usually done by seasonal time series models. To give an example for this approach we analyze the electricity demand per quarter of an hour of industrial customers in Germany. The necessity of such predictions results from the liberalization of the German electricity market in 1998 due to legal requirements of the EC in 1996.     

中国用电单耗变化趋势及其影响因素实证分析

以中国国民经济用电单耗为研究对象,采用描述性统计法和因素分解法相结合的方法,实证分析1990—2007年间中国GDP用电单耗和三次产业用电单耗的变化趋势,并对影响用电单耗变化的结构因素和效率因素的贡献值和贡献份额进行定量计算,结果表明：GDP用电单耗的下降主要是由于电源使用效率的提高引起的,其中二产用电单耗的变化是决定性因素。     

Short-term forecasting with a computationally efficient nonparametric transfer function model

In this paper a semi-parametric approach is developed to model non-linear relationships in time series data using polynomial splines. Polynomial splines require very little assumption about the functional form of the underlying relationship, so they are very flexible and can be used to model highly non-linear relationships. Polynomial splines are also computationally very efficient. The serial correlation in the data is accounted for by modelling the noise as an autoregressive integrated moving average (ARIMA) process, by doing so, the efficiency in nonparametric estimation is improved and correct inferences can be obtained. The explicit structure of the ARIMA model allows the correlation information to be used to improve forecasting performance. An algorithm is developed to automatically select and estimate the polynomial spline model and the ARIMA model through backfitting. This method is applied on a real-life data set to forecast hourly electricity usage. The non-linear effect of temperature on hourly electricity usage is allowed to be different at different hours of the day and days of the week. The forecasting performance of the developed method is evaluated in post-sample forecasting and compared with several well-accepted models. The results show the performance of the proposed model is comparable with a long short-term memory deep learning model.     

基于系统动力学的银行卡刷卡消费价格策略研究

银行卡刷卡消费系统是一个由网络运营商、发卡行、收单行、商户和消费者五个主体及相互作用组成的网络系统。为了使刷卡消费系统的定价策略能够有益于系统的长期发展,解决对刷卡消费价格的分析仅限于静态方面研究的局限,运用VENSIM软件,建立银行卡刷卡消费定价策略的系统动力学模型,并对模型的运行和模拟结果进行了动态分析,实证分析了银行卡刷卡消费各主体不同价格策略对刷卡消费主要指标的影响。     

中国能源消耗和环境污染与经济发展的灰色关联分析

利用灰色关联分析分别对能源消耗和环境污染治理投资对经济发展的影响以及能源消耗对环境污染治理投资的影响进行了分析.结果表明水电的消耗和环境污染治理投资对经济发展影响较大,而水电的消耗又对环境污染治理投资的影响比较大,这与中国目前的状况相符合.     

A Framework for Time Varying Parameter Regression Modeling

A framework for time varying parameter regression models is developed and employed in modeling and forecasting price expectations, using the Livingston data. Alternative model formulations, which include various choices for both the stochastic processes generating the varying parameters and the sets of explanatory variables, are examined and compared by using this framework. These models, some of which have appeared elsewhere and some of which are new, are estimated and used to assess the expectations formation process.     

An Interactive Forecasting System

Time sharing computer configurations have introduced a new dimension in applying statistical and mathematical models to sequential decision problems. When the outcome of one step in the process influences subsequent decisions, then an interactive time-sharing system is of great help. Since the forecasting function involves such a sequential process, it can be handled particularly well with an appropriate time-shared computer system. This paper describes such as system which allows the user to do preliminary analysis of his data to identify the forecasting technique or class of techniques most appropriate for his situation and to apply those in developing a forecast. This interactive forecasting system has met with excellent success both in teaching the fundamentals of forecasting for business decision making and in actually applying those techniques in management situations.     

Intertemporal consumer behaviour under structural changes in income

In this paper we analyze models of forward looking consumer behaviour and give empirical evidence for aggregate quarterly data for the Netherlands, 1967-1984. Special attention is devoted to the implications of unanticipated structural changes in the income process, which because of replanning, will have an impact on the consumption decision. We start with the life cycle hypothesis. Since the fall in aggregate consumption in the Netherlands in the eighties can not be explained by the life cycle model, the theory is reformulated by assuming that the planning horizon of the consumers moves ahead as time goes on. As a result, an error correction mechanism has to be introduced in the consumption function.The modified model is found to be in agreement with the data.