Short-term load forecasting coupled with weather profile generation methodology

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

11 Scopus Citations
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Original languageEnglish
Pages (from-to)310-327
Journal / PublicationBuilding Services Engineering Research and Technology
Issue number3
Online published6 Nov 2017
Publication statusPublished - 1 May 2018


Short-term building load forecasting is indispensable in daily operation of future intelligent/green buildings, particularly in formulating system control strategies and assessing the associated environmental impacts. Most previous research works have been focused on studying the advancement in forecasting techniques, but not as much on evaluating the availability of influential factors like the predicted weather profile in the coming hours. This article proposes an improved procedure to predict the building load 24 hours ahead, together with a backup weather profile generating method. The quality of the proposed weather profile generation model and the forecasting procedures were examined through a case study of application to university academic buildings. The results showed that the load forecasting accuracy with the application of either the real weather data on record or of the predicted weather data from the profile generation model is very much similar. This indicates that the weather prediction model is suitable for applying to building load forecasting. Besides, the comparisons between different sets of input data illustrated that the forecasting accuracy can be improved through the input data filtering and regrouping procedures. Practical application : A weather profile prediction technique for use in building energy forecasting was introduced. This can be coupled to a building energy use forecasting model for predicting the hourly consumption profile of the next day. This prediction time span can be crucial for formulating the daily operation plan of the utility systems or for smart micro-grid applications. The appropriateness of the methodology was evaluated through a case study.

Research Area(s)

  • Building energy consumption, data filtering and regrouping, short-term load forecasting, weather forecasting