Lookup NU author(s): Dr Yaodong Wang,
Dr Dawei Wu,
Professor Tony Roskilly
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
The nature of domestic electricity load is highly dependent on the demand of occupants. Domestic energy use, especially for electricity, is not only based on residents activities, also related with the type of electrical appliances and weather conditions. To manage and optimise electricity generation and the effective use of energy storage, it is important to be able to accurately predict electricity demand. This paper presents high-resolution real load energy data for three UK dwellings throughout the year. Seasonal models have been produced for each dwelling and the use of electrical appliances at certain times are analysed to predict the number of active occupants. The possibility of active occupancy at each thirty seconds is generated stochastically by Markov-Chain technique and Markov-Chain Monte Carlo method is used to predict the active occupant profiles and the related electricity demand dynamically. The methodology can be used for any other domestic dwelling type to generate corresponding active occupant profile. The predicted electricity profile can be used for effective demand side management. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
Author(s): Li YP, Wang YD, Wu DW, Chen HS, Sui J, Zhang XJ, Roskilly AP
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 6th International Conference on Applied Energy (ICAE 2014)
Year of Conference: 2014
Online publication date: 12/01/2015
Acceptance date: 01/01/1900
Date deposited: 05/08/2019
Publisher: Elsevier BV
Series Title: Energy Procedia