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Energy
Article . 2018 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector

Authors: E.J. Palacios-Garcia; A. Moreno-Munoz; I. Santiago; J.M. Flores-Arias; F.J. Bellido-Outeirino; I.M. Moreno-Garcia;

A stochastic modelling and simulation approach to heating and cooling electricity consumption in the residential sector

Abstract

Heating and cooling consumption is one of the most significant terms in the total supply, which may come to represent half of the total demand in European countries. These appliances are supplied by a wide range of sources, being electrical devices of special interest in the Smart Grid. Current tools allow the assessment of the consumption with a high accuracy, nevertheless, they lack the temporal resolution or low-level details to study advanced control techniques. In this context, bottom-up stochastic models are a main tool to simulate high-resolution demand profiles. This paper presents a 1-min resolution model for electricity demand of heating and cooling appliances. The system is based on the simulation of individual households considering variables such as the number of residents, location, type of day (weekday or weekend) and date. It was used to simulate daily profiles which showed two main demand peaks, one during mornings and another during dinner time, for heating, and a high demand during midday for cooling consumption. Moreover, annual simulations depicted the importance of cooling appliances, which despite having a lower annual demand, can overcharge the grid with their concurrent utilisation, highlighting the usefulness of this tool for studying the impact of these devices.

Country
Spain
Keywords

550, Residential electricity demand, Occupancy, Smart grid, Stochastic models, Cooling consumption, Heating consumption

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    27
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
27
Top 10%
Top 10%
Top 10%
Green