Online short-term heat load forecasting for single family houses

Peder Bacher, Henrik Madsen, Henrik Aalborg Nielsen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

This paper presents a method for forecasting the load for heating in a single-family house. Both space and hot tap water heating are forecasted. The forecasting model is built using data from sixteen houses in Sønderborg, Denmark, combined with local climate measurements and weather forecasts. Every hour the hourly heat load for each house the following two days is forecasted. The forecast models are adaptive linear time-series models and the climate inputs used are: ambient temperature, global radiation, and wind speed. A computationally efficient recursive least squares scheme is used. The models are optimized to fit the level of optimal adaptivity and the thermal dynamical response of the building. Identification of a model, which is suitable for application to all the houses, is carried out. The results show that the forecasting errors mainly are related to: unpredictable high frequency variations in the heat load signal (predominant only for some houses), peaks presumably from showers, shifts in resident behavior, and uncertainty of the weather forecasts for longer horizons, especially for the solar radiation.
Original languageEnglish
Title of host publication39th Annual Conference of the IEEE Industrial Electronics Society, IECON 2013
PublisherIEEE
Publication date2013
Pages5741-5746
DOIs
Publication statusPublished - 2013
EventIECON 2013: 39th Annual Conference of the IEEE Industrial Electronics Society - Vienna, Austria
Duration: 10 Nov 201313 Nov 2013

Conference

ConferenceIECON 2013
CountryAustria
CityVienna
Period10/11/201313/11/2013
SeriesProceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN1553-572X

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