Electricity demand forecasting for decentralised energy management

作     者：Sean Williams Michael Short 

作者机构：School of ComputingEngineering&Digital TechnologiesTeesside UniversityTS13BXUK 

出 版 物：《Energy and Built Environment》 (能源与人工环境（英文）)

年 卷 期：2020年第1卷第2期

页      面：178-186页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The first author wishes to acknowledge the financial support pro-vided by Teesside University and the Doctoral Training Alliance(DTA)scheme in Energy.The authors also acknowledge elements of the work was carried out as part of the REACT project(01/01/2019-31/12/2022)which is co-funded by the EU’s Horizon 2020 Framework Programme for Research and Innovation under Grant Agreement No.824395 

主　　题：Demand response Decentralised Grid edge Time series forecasting 

摘      要：The world is experiencing a fourth industrial *** development of technologies is advancing smart infrastructure *** observe decarbonisation,digitalisation and decentralisation as the main drivers for *** electrical power systems a downturn of centralised conventional fossil fuel fired power plants and increased proportion of distributed power generation adds to the already troublesome outlook for op-erators of low-inertia energy *** the absence of reliable real-time demand forecasting measures,effective decentralised demand-side energy planning is often *** this work we formulate a simple yet highly effective lumped model for forecasting the rate at which electricity is *** methodology presented focuses on the potential adoption by a regional electricity network operator with inadequate real-time energy data who requires knowledge of the wider aggregated future rate of energy ***,contributing to a reduction in the demand of state-owned generation power *** forecasting session is constructed initially through analysis of a chronological sequence of discrete *** demand data shows behaviour that allows the use of dimensionality reduction *** with piecewise interpolation an electricity demand forecasting methodology is *** of short-term forecasting problems provide credible predictions for energy *** for medium-term forecasts that extend beyond 6-months are also very *** forecasting method provides a way to advance a novel decentralised informatics,optimisa-tion and control framework for small island power systems or distributed grid-edge systems as part of an evolving demand response service.