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On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models
Fay, Damien; Ringwood, John
Weather information is an important factor in load forecasting models. Typically, load forecasting models are constructed and tested using actual weather readings. However, online operation of load forecasting models requires the use of weather forecasts, with associated weather forecast errors. These weather forecast errors inevitably lead to a degradation in model performance. This is an important factor in load forecasting but has not been widely examined in the literature. The main aim of this paper is to present a novel technique for minimizing the consequences of this degradation. In addition, a supplementary technique is proposed to model weather forecast errors to reflect current accuracy. The proposed technique utilizes a combination of forecasts from several load forecasting models (sub-models). The parameter estimation may thus be split into two parts: sub-model and combination parameter estimation. It is shown that the lowest PMSE corresponds to training the sub-models with actual weather but training the combiner with forecast weather.
Keyword(s): Electronic Engineering; Load forecasting; model combination; neural networks; weather forecast errors
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Institution: Maynooth University
Citation(s): Fay, Damien and Ringwood, John (2010) On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models. IEEE Transactions on Power Systems, 25 (3). pp. 1751-1758. ISSN 0885-8950
Publisher(s): Institute of Electrical and Electronics Engineers (IEEE)
File Format(s): application/pdf
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First Indexed: 2014-09-20 05:03:35 Last Updated: 2018-05-15 06:10:15