An Inherently Updatable On-line Load Forecasting Technique

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Short term load forecasting is an important and complex task requiring in-depth understanding of the technique and the system. Many statistical and expert system based techniques produce fairly accurate forecast. However, most statistical techniques are computationally burdensome as these methods need updating with changing conditions. Expert systems are more adaptable to changing conditions, but it is not always easy to express the available expertise (knowledge) in clear quantitative terms, often leading to inconsistent rules. The objective of this research is to develop a new load forecasting technique which combines the capabilities of both the expert systems approach and the statistical techniques, but avoids their drawbacks. Expert systems approach is adopted to utilize the available body of knowledge to form a subset of historical data which are most pertinent to the forecast point. The priority vector method, explained in the proposal, is applied to quantitatively express any qualitative factors involved in the process. This approach is fundamentally different from the others, because we create subsets of historical data according to the conditions at the forecast point, instead of the traditional approach of analyzing the whole historical data set. Further, the updating function becomes automatic in our technique since the subset of data is continually reconstructed and the relationships redetermined to match each forecast point. The development of the technique in its simplest form is outlined and the application is demonstrated in weekly load forecast. We have identified and briefly discussed the areas which need further work to make the model sophisticated for better performance, and to extend it to perform interval forecasting.