SOCIAL ACCOUNTING MATRIX FOR INDIA

This paper provides the latest Social Accounting Matrix (SAM) of the year 2003–2004 for the Indian economy with a wide variety of disaggregation for the Energy sector and the sectors that are relevant for environmental and climate policy evaluation. This SAM shows the interaction between production, income, consumption and capital accumulation. It can be used to provide an analysis of the interrelationship between the production structure of an economy and the distribution of incomes and expenditures of different household groups. In addition, it can be used for multiplier analysis to capture direct, indirect and induced impact on input use due to any exogenous changes in the economy. This SAM consists of 85 sectors of the economy, three factors of production and nine categories of occupational households. The Indian economy is becoming structurally biased towards capital intensive sectors, such as service and energy production. The energy production sector itself is the most energy intensive sector as of 2003–2004.     

浅层地温热能在设施农业中的应用研究——以天津市宁河县潘庄镇地区为例

本文介绍了温室冬季供热采用浅层地热能作为热源,地源热泵供热系统作为加热方式的应用研究。本课题旨在通过浅层地热能资源调查研究和地源热泵技术利用适宜性研究,查明天津市宁河县潘庄镇齐心庄村土地开发整理项目区的地温能源分布特征、资源量、地源热泵提取地温热能的适宜性及其各项技术参数,为土地开发整理的综合规划设计、长期发展战略和制定相关管理政策提供依据,实现科学、合理、慎重、有效地提高土地整理后的土地高效利用之目的。分析结果表明,浅层地温供热系统比传统供热系统节能28.8%。实验温室供热系统COP值为3.79,与采用燃煤供热系统的温室相比,温室相对节能47.4%。     

“多项连乘和加总”的因素分解法在能源平衡表中的应用

本文对各种研究方法进行分类比较,并指出它们之间的相通性;接着指出在实证研究中普遍存在由于经济部门、产业结构、能源种类等划分的宽口径带来的实验误差：宽口径处理方法的盛行一方面是由于详尽数据的获得存在难度;另一方面,是因为国内外现有研究都是采取“单项连乘和”的形式,这极大限制了分解表达式的涵盖范围和因子种类。为了克服实证研究误差,本文提出将“单项连乘和”扩展成“多项连乘和的加总”形式,并对这种新的方程形式进行因素分解的计算和求解,最后将新方法运用到北京市2000年-2011年的能源平衡表中,分解终端能源消费,获得了细致合理的实验结果。     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

The Global Energy Forecasting Competition 2017 (GEFCom2017) attracted more than 300 students and professionals from over 30 countries for solving hierarchical probabilistic load forecasting problems. Of the series of global energy forecasting competitions that have been held, GEFCom2017 is the most challenging one to date: the first one to have a qualifying match, the first one to use hierarchical data with more than two levels, the first one to allow the usage of external data sources, the first one to ask for real-time ex-ante forecasts, and the longest one. This paper introduces the qualifying and final matches of GEFCom2017, summarizes the top-ranked methods, publishes the data used in the competition, and presents several reflections on the competition series and a vision for future energy forecasting competitions.     

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.     

山东省能源效率评价分析

为提高山东省能源效率,实现节能目标,需要把握当前的能源利用状况、能源效率变动的趋势以及能源效率变化的影响因素。本文基于能源效率的内涵,在分析借鉴国内外现有的能源效率指标体系的基础上,结合山东省的能源环境实际情况与特点,构建了山东省能源效率评价指标体系,运用因子分析方法,对山东省能源效率进行分析并做出综合评价。     

基于城市生活载体（房屋）提供节能减排服务研究

在我国快速推进城镇化中,城市的能源消耗和污染排放问题不断恶化。要想有效控制和改善这些问题应该从城市生活的主要载体--房屋入手,在房屋的使用过程中加强节能减排工作。而市场化的物业管理是当前房屋管理的主要形式,所以以物业管理项目作为整合载体加强节能减排工作是十分必要和有效的。同时,针对节能减排的公共服务属性,提出了政府调控的措施建议。     

对住宅建筑规划与设计的节能措施探讨

随着社会环境问题的日益严峻以及相关环保产业大力发展,对住宅建筑规划与设计的节能措施的探讨越来越成为相关行业研究的热点问题。本文重点探讨住宅建筑规划与设计中的一些常用节能措施以及这些措施在实际操作中的一些限制因子。     

新能源基地建设的实践模式与经验借鉴

在世界主要国家竞相发展新能源技术、抢占新一轮经济和科技竞争制高点背景下,新能源产业正成为最富活力、最具前景的战略性新兴产业.系统总结国内外先进新能源基地建设的实践模式发现,新能源基地建设需要加强组织领导、实施新能源基地发展战略,创新体制机制、设立新能源基地建设专项基金,依托特色优势、打造新能源产业发展战略高地,培育龙头...