Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385049
Title: Forecasting electricity demand in the industrial sector based on disaggregate data
Author: McCafferty, Peter
ISNI:       0000 0001 3622 6747
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 1991
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Abstract:
The objective of this work was to investigate the use of disaggregation in the development of models for forecasting the electrical energy demand in the industrial sector. A variety of modelling techniques were examined and models were developed based on past demand data and external variables. A number of different types of disaggregation were investigated and it was found that grouping based on the standard industrial classification codes resulted in the optimum models. The combination of the predictions from these models resulted in more accurate forecasts than the aggregate model for the total industrial sector for short lead times. The response of electrical demand to external variables in the different industries was investigated and when included in the model no significant increase in accuracy was found. However when these inputs were included for the modelling of individual consumers, a significant improvement was obtained. This was associated with the quality of external data available for individual consumers. The identification of outliers and the use of intervention analysis showed significant improvements in the models developed for all levels of disaggregation and was found to deal adequately with the opening and closure of large industries. Forecasting on a half hour basis was also examined and at this level intervention analysis was again found to be most appropriate. Based on half hour forecasting techniques, a novel design of a maximum demand controller was proposed and the performance assessed under typical conditions.
Supervisor: Beattie, W. C. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.385049  DOI: Not available
Keywords: Energy conservation & Energy consumption
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