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.     

The moderating effect of innovation on the relationship between environmental and financial performance: Evidence from high emitters in Australia

This study examines whether environmental-related innovation moderates the association between environmental and financial performance measured respectively as carbon emissions and return on assets (ROA). The sample comprises 119 companies subject to Australia's National Greenhouse Energy Reporting Act (NGER) for the period 2009–2017. The results show that environmental innovation positively moderates the relationship between environmental performance and financial performance. The findings also imply that the impact of environmental innovation in improving environmental performance is observable with a 1-year lag. The results are robust to the alternative financial performance measures of Tobin's Q and Altman's Z score. The findings have important implications for company managers and policymakers and provide useful information on innovation's role in enhancing environmental and financial performance.     

Energy intensity and directed fiscal policy

This paper assesses the effects of fiscal policy on economy-wide energy intensity within an endogenous growth framework. To this end, we first develop a two-sector (investment good and consumption good) augmented AK model by integrating the Uzawa model with Rebelo’s AK model, and assume that a non-renewable resource is one of the factors of production. Using this framework, we solve the model for the short and long run, identifying the sufficient parameter conditions that ensure higher energy intensity in the investment goods sector. We then introduce a balanced budget government, whose objective is to decrease the economy-wide energy intensity by levying tax on the energy-intensive investment goods sector and subsidizing the consumption goods sector. Contrary to our expectations, we find that this fiscal policy design increases economy-wide energy intensity as it leads to a decline in real GDP without changing total energy consumption. On the basis of this model, we propose the concept of a ‘directed fiscal policy’, which connotes a reduction of the economy-wide energy intensity by following a heterogeneous taxation policy across sectors.     

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。     

北京等四直辖市能源利用效果的对比分析——描述统计与面板数据研究

本文基于1985-2006年中国北京、天津、上海、重庆四大直辖市的经济与能源统计数据,利用人均能源终端消费量、单位GDP能耗、能源消费弹性系数等描述性统计方法对中国地区能源利用效果进行了比较研究,并基于FGLS估计方法和Swamy变系数面板数据模型,对四大直辖市能源利用状况的扩张趋势以及能源利用水平的差异进行了测度,并提出相应政策建议.     

城市化与能源消费关系的动态计量分析——以陕西省电力消费为例

运用经济计量方法,实证了陕西省城市化与能源消费之间的动态相关性,并利用因素分解模型定量测算出陕西城市化对能源消费变动的贡献份额.结果表明,陕西省城市化与能源消费量之间存在协整关系.但这种长期均衡的短期调整幅度不大;现阶段城市化对陕西能源需求的贡献作用尚比较小.且呈现逐年下降的趋势.因此,能源需求不会成为制约陕西省城市化进程中的瓶颈.     

“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。     

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。