AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING

Abstract The increased reliance on demand‐side management policies as an urban water consumption management tool has stimulated considerable debate among economists, water utility managers, regulators, consumer interest groups and policymakers. In turn, this has fostered an increasing volume of literature aimed at providing best‐practice estimates of price and income elasticities, quantifying the impact of non‐price water restrictions and gauging the impact of non‐discretionary environmental factors affecting residential water demand. This paper provides a synoptic survey of empirical residential water demand analyses conducted in the last 25 years. Both model specification and estimation and the outcomes of the analyses are discussed.     

A hybrid machine learning model for forecasting a billing period’s peak electric load days

Many models have been studied for forecasting the peak electric load, but studies focusing on forecasting peak electric load days for a billing period are scarce. This focus is highly relevant to consumers, as their electricity costs are determined based not only on total consumption, but also on the peak load required during a period. Forecasting these peak days accurately allows demand response actions to be planned and executed efficiently in order to mitigate these peaks and their associated costs. We propose a hybrid model based on ARIMA, logistic regression and artificial neural networks models. This hybrid model evaluates the individual results of these statistical and machine learning models in order to forecast whether a given day will be a peak load day for the billing period. The proposed model predicted 70% (40/57) of actual peak load days accurately and revealed potential savings of approximately USD $80,000 for an American university during a one-year testing period.     

ECONOMIC EXPERIMENTS,HYPOTHETICAL SURVEYS AND MARKET DATA STUDIES OF INSURANCE DEMAND AGAINST LOW‐PROBABILITY/HIGH‐IMPACT RISKS: A SYSTEMATIC REVIEW OF DESIGNS,THEORETICAL INSIGHTS AND DETERMINANTS OF DEMAND

This paper provides a systematic review of the literature on 80 experimental, hypothetical survey and market data studies of insurance demand against low‐probability/high‐impact risks. The objective of the review is to extract lessons from these studies and to outline an agenda for future research. We contrast the results of experimental and survey studies to findings from market data. We focus on experimental design methods, insurance characteristics, as well as results about theories, heuristics, behavioural biases and explanatory variables. Lessons for policymakers are drawn which can facilitate disaster preparedness.     

纯分时定价与分时阶梯定价对政策目标实现的对比分析

基于双层需求模型,对分时阶梯定价与纯分时定价下的居民电力需求行为进行比较。利用相对需求方程检验两类定价下的相对需求是否具有非位似偏好性质,运用绝对需求方程分析居民在峰谷时的不同弹性特征。研究结论表明,分时阶梯与纯分时定价下存在不同的收入和电费位似偏好性质;居民的价格与需求弹性显著不同;两类定价下可以实现的政策目标也存在明显差异。本文为系统比较非线性定价与线性定价下的需求奠定了基础。     

Forecasting electricity prices with expert,linear, and nonlinear models

This paper compares several models for forecasting regional hourly day-ahead electricity prices, while accounting for fundamental drivers. Forecasts of demand, in-feed from renewable energy sources, fossil fuel prices, and physical flows are all included in linear and nonlinear specifications, ranging in the class of ARFIMA-GARCH models—hence including parsimonious autoregressive specifications (known as expert-type models). The results support the adoption of a simple structure that is able to adapt to market conditions. Indeed, we include forecasted demand, wind and solar power, actual generation from hydro, biomass, and waste, weighted imports, and traditional fossil fuels. The inclusion of these exogenous regressors, in both the conditional mean and variance equations, outperforms in point and, especially, in density forecasting when the superior set of models is considered. Indeed, using the model confidence set and considering northern Italian prices, predictions indicate the strong predictive power of regressors, in particular in an expert model augmented for GARCH-type time-varying volatility. Finally, we find that using professional and more timely predictions of consumption and renewable energy sources improves the forecast accuracy of electricity prices more than using predictions publicly available to researchers.     

EFFECTS OF THE 2011 GREAT EAST JAPAN EARTHQUAKE ON ELECTRICITY LOAD DEMAND: STRUCTURAL CHANGES DURING RECOVERY PERIODS

In response to the Fukushima Daiichi Nuclear Power Plant's severe crisis, the Tokyo Electric Power Company planned rolling blackouts, and the Japanese government encouraged companies and residents to conserve electricity by adopting self‐restriction plans. We examine the structural changes caused by the disaster in Japan and the effects of the power blackouts and self‐restriction plans on the magnitude and pattern of load demand. The results show that the total demand decreased after the disaster and changed from weekdays to weekends and holidays. In addition, the effect of temperature on load demand changed after the disaster.     

THE DEMAND FOR BEER,WINE AND SPIRITS: A SURVEY OF THE LITERATURE

Abstract The demand for alcohol literature is vast and much conflicting information about the nature of the demand for alcoholic beverages has been published. This paper presents a survey of the literature, and then uses the technique of meta‐regression analysis to establish insights into the nature of the demand for beer, wine and spirits. Unlike previous meta‐studies of the demand for alcoholic beverages this study adjusts for the precision of each elasticity estimate. The analysis presented suggests reported elasticity estimates will be influenced by such factors as estimation technique, data frequency and time period under consideration. With respect to time, the findings suggest that the demand for alcoholic beverages has become less inelastic since the mid‐1950s and that the income elasticity has been falling since the mid‐1960s. The analysis also found support for the idea that alcohol as a commodity group is a necessity, and that consumers respond to price discounting with inventory behaviour rather than true substitution behaviour. Little support is found for the idea that the demand for alcoholic beverages varies fundamentally across most countries, although wine may be an exception.     

PROMOTING INNOVATION IN THE ELECTRICITY INDUSTRY

Smart metering can bring significant benefits to electricity markets by allowing customers to reduce demand or increase supply when generation capacity is temporarily scarce. To reap the full efficiency and environmental benefits of this technology, regulators must allow price volatility and free entry into the market. The efficiency gains are enormous as both demand and supply will be affected by both temporary and longer‐lasting price changes. Experiments have shown the value of this approach.     

TECHNOLOGICAL INNOVATION AND EMPLOYMENT IN DERIVED LABOUR DEMAND MODELS: A HIERARCHICAL META‐REGRESSION ANALYSIS

The effect of technological innovation on employment is of major concern for workers and their unions, policy makers and academic researchers. We meta‐analyse 570 estimates from 35 primary studies that estimate a derived labour demand model. We contribute to existing attempts at evidence synthesis by addressing the risks of selection bias and that of data dependence in observational studies. Our findings indicate that: (i) hierarchical meta‐regression models are sufficiently versatile for addressing both selection bias and data dependence in observational data; (ii) innovation's effect on employment is positive but small and highly heterogeneous; (iii) only a small part of residual heterogeneity is explained by moderating factors; (iv) selection bias tends to reflect preference for upholding prevalent hypotheses on the employment effects of process and product innovations; (v) country‐specific effect‐size estimates are related to labour market and product market regulation in six OECD countries in a U‐shaped fashion; and (vi) OLS estimates reflect upward bias whereas those based on time‐differenced or within estimators reflect a downward bias. Our findings point out to a range of data quality and modelling issues that should be addressed in future research.     

中国电信市场的需求关系估计与需求效应分析

本文采用全国31个省市自治区2002～2004年的Panel观测数据,运用两阶段固定效应法,估计了中国电信市场的常弹性需求函数。估计结果显示,电信服务的价格水平、移动电话用户的数量、城镇居民人均可支配收入、互联网接入用户的数量和通信工具的相对价格等5个因素,对中国电信市场的需求具有统计上的显著影响。但从实际需求效应上看,对中国电信市场业务总量增幅的贡献主要来自于电信服务价格水平的大幅度下降,以及移动电话用户数量的急剧增长。估计结果支持,中国电信市场经历了一个市场结构消除垄断、电信业务移动化和消费对象大众化的过程。可以说,正是市场竞争程度的大幅度提升,以及由此触发的国民消费观念的转变,推动了中国电信市场需求的高速增长。     

Improving Electricity Market Price Forecasting with Factor Models for the Optimal Generation Bid

In liberalized electricity markets, the electricity generation companies usually manage their production by developing hourly bids that are sent to the day‐ahead market. As the prices at which the energy will be purchased are unknown until the end of the bidding process, forecasting of spot prices has become an essential element in electricity management strategies. In this article, we apply forecasting factor models to the market framework in Spain and Portugal and study their performance. Although their goodness of fit is similar to that of autoregressive integrated moving average models, they are easier to implement. The second part of the paper uses the spot‐price forecasting model to generate inputs for a stochastic programming model, which is then used to determine the company's optimal generation bid. The resulting optimal bidding curves are presented and analyzed in the context of the Iberian day‐ahead electricity market.     

Competition and Innovation in Retail Electricity Markets: Evidence from Italy

Italy's 2015 Annual Competition Law, if finally approved, provides for phasing out retail electricity price regulation, as well as the implementation of full retail liberalisation, from 1 July 2018. This is a significant reform, not just because it is consistent with the broader market design for electricity. Indeed, retail liberalisation is a qualifying element of the full integration of the European Union's electricity market. The full opening of retail markets provides a great opportunity for innovation, both on the demand side and on the supply side. This article investigates the theoretical background, and presents some empirical evidence, on the competition–innovation nexus in retail electricity markets.     

Physician Incentives and Treatment Choice

I analyze the impact of physician competition for patients on treatment selection and an insurer's ability to induce its preferences through a supply‐side payment mechanism. Informed patients choose the physician whose treatment practice best fits their preferences, aligning physician incentives with patient preferences. An insurer's ability to counter these incentives is not monotonic in how informed patients are, however. When demand is either perfectly inelastic to treatment practices because patients are completely uninformed or a sufficient proportion of the market is informed relative to the number of physicians in the market, then an insurer can induce its preferences through supply‐side payment rules. Otherwise, more intensive policy levers such as utilization review must be employed. Programs that increase patient information can therefore improve the efficiency despite generating stronger incentive to treat according to patient preferences. I also explore how noisy signals of illness type and diagnostic testing further complicate the insurer's problem.     

海西高职院校电力营销人才培养定位与对策研究

随着海峡西岸经济建设的高速发展,电力行业现代化管理不断深人,对电力营销人才无论在量还是质的方面都提出了新的更高的要求。本课题以福建电力企业对电力营销人员的实际需求为出发点,探讨如何培养出适应海西电力市场改革与发展需要的电力营销高职人才的方案。文章首先通过广泛的调研和分析,准确及时地了解海西电力市场对营销人才数量、质量、能力等各方面的需求;然后,根据电力市场需要和高等教育的规律准确科学地对高职院校电力营销进行专业定位;在此基础上,遵循教学规律探索出适应电力体制改革新形势的电力营销专业高职人才培养新模式。     

STOCK‐FLOW INTERACTIONS,DISEQUILIBRIUM MACROECONOMICS AND THE ROLE OF ECONOMIC POLICY

This paper presents the ‘KMGT’ (Keynes–Metzler–Goodwin–Tobin) portfolio model and studies its stability properties. The approach to macrodynamic modelling taken here extends the KMG model of Chiarella and Flaschel (2000) , focusing in particular on the incorporation of financial markets and policy issues. The original KMG model considered three asset markets (equities, bonds and money) but depicted them in a rudimentary way so that they had little influence on the real side of the model. The only financial market influencing the real side of the economy was the money market (via an LM curve theory of interest). Here Tobin's portfolio choice theory models the demand for each asset in such a way that the total amount of assets that households want to hold equals their net wealth, which is a stock constraint attached to portfolio choice. There is also a flow constraint, that the net amount of assets accumulated (liabilities issued) by one sector must equal its net savings (expenditures). The Tobinian macroeconomic portfolio approach characterizes the potential for financial market instability, focusing on the interconnectedness of all three markets. The paper goes on to study the potential for labour market and fiscal policies to stabilize unstable macroeconomies.     

CONDITIONAL DURATION MODELS FOR HIGH‐FREQUENCY DATA: A REVIEW ON RECENT DEVELOPMENTS

This paper reviews the recent literature on conditional duration modeling in high‐frequency finance. These conditional duration models are associated with the time interval between trades, price, and volume changes of stocks, traded in a financial market. An earlier review by Pacurar provides an exhaustive survey of the first and some of the second generation conditional duration models. We consider almost all of the third‐generation and some of the second‐generation conditional duration models. Notable applications of these models and related empirical studies are discussed. The paper may be seen as an extension to Pacurar.     

Structural Analysis of Electricity Demand and Supply Interactions*

We specify a structural asymmetric vector error‐correction model to identify and estimate the demand and supply functions in hourly day‐ahead wholesale electricity markets. In doing so, we provide, inter alia, new insights into a well‐established but unresolved issue concerning the extent of the demand elasticity to price in these markets. We show that whilst demand appears to be inelastic in the short‐run, the quantity traded on the market is significantly influenced by the price level and responds to previous disequilibria in the supply curve through an asymmetric error‐correction mechanism, reacting to a positive disequilibrium but not to a negative one.     

GEFCom2012: Electric load forecasting and backcasting with semi-parametric models

We sum up the methodology of the team tololo for the Global Energy Forecasting Competition 2012: Load Forecasting. Our strategy consisted of a temporal multi-scale model that combines three components. The first component was a long term trend estimated by means of non-parametric smoothing. The second was a medium term component describing the sensitivity of the electricity demand to the temperature at each time step. We use a generalized additive model to fit this component, using calendar information as well. Finally, a short term component models local behaviours. As the factors that drive this component are unknown, we use a random forest model to estimate it.     

A general method for addressing forecasting uncertainty in inventory models

In practice, inventory decisions depend heavily on demand forecasts, but the literature typically assumes that demand distributions are known. This means that estimates are substituted directly for the unknown parameters, leading to insufficient safety stocks, stock-outs, low service, and high costs. We propose a framework for addressing this estimation uncertainty that is applicable to any inventory model, demand distribution, and parameter estimator. The estimation errors are modeled and a predictive lead time demand distribution obtained, which is then substituted into the inventory model. We illustrate this framework for several different demand models. When the estimates are based on ten observations, the relative savings are typically between 10% and 30% for mean-stationary demand. However, the savings are larger when the estimates are based on fewer observations, when backorders are costlier, or when the lead time is longer. In the presence of a trend, the savings are between 50% and 80% for several scenarios.     

Daily peak electrical load forecasting with a multi-resolution approach

In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders in the energy industry. An understanding of peak magnitude and timing is paramount for the implementation of smart grid strategies such as peak shaving. The modelling approach proposed in this paper leverages high-resolution and low-resolution information to forecast daily peak demand size and timing. The resulting multi-resolution modelling framework can be adapted to different model classes. The key contributions of this paper are (a) a general and formal introduction to the multi-resolution modelling approach, (b) a discussion of modelling approaches at different resolutions implemented via generalised additive models and neural networks, and (c) experimental results on real data from the UK electricity market. The results confirm that the predictive performance of the proposed modelling approach is competitive with that of low- and high-resolution alternatives.