AN EXPERT SYSTEM BASED ALGORITHM FOR SHORT TERM LOAD FORECAST
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Joined: Sep 2010
14-10-2010, 11:14 AM
This paper reviews some of the existing studies on one-to-twenty four hour load forecasting algorithms, and presents an expert system based algorithm as an alternative. The logical and syntactical relationships between weather and load, and the prevailing daily load shapes have been examined to develop the rules for this approach. Two separate, but similar, algorithms have been developed to provide one-to-six hour and 24 hour ahead forecasts. These forecasts have been compared with observed hourly load data for a Virginia electric utility for all seasons of the year. The one and six hour ahead forecast errors (absolute average) ranged from 0.869% to 1.218%. and from 2.437% to 3.48% respectively. The 24-hour ahead forecast errors (absolute average) ranged from 2.429% to 3.300%.
Load forecast plays an important role in all aspects of electric utility operations. The forecasting methodology and the requirements of the load forecast, such as accuracy and detail are. of course, dependent on the function of the load forecast. The short term (one to twenty-four hour) load forecast is of importance in the daily operations pf the utility. It is required for unit commitment, energy transfer scheduling and the load dispatch. With the emergence of Load Management, the short term load forecast has a broader role in utility operations - it is also required for the coordination of Load Management programs with conventional system resources. Since the effhctiveness of Load Management programs is sensitive to the system load, this additional function places higher accuracy requirements on the short term forecast.
Additionally, the electric utility is no longer the only interested party in the short term load forecast. System peak coincident demand charges and rate structures designed to encourage Load Management programs offer the potential of considerable savings to large industrial customers and electric cooperatives. With advance knowledge of the electric utility load, customers can sohedule Load Management activities to
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