A Data Mining Approach for Spatial Modeling in Small Area Load Forecast

In a competitive power market, locations of future load growth have to be described with sufficient geographic precision to permit valid marketing strategy and siting of future T&D equipment. Small area load forecast, which provides information of future electric demand that includes spatial and temporal characteristics, is useful for T&D and market planning. Domain experts for spatial load forecast require long-term practicing and are difficult to find. In order to capture the meaningful associations between spatial data and the load changes, and to provide a useful tool for spatial load forecast, a data mining technique based on a knowledge discovery in database (KDD) procedure is proposed to determine automatically the preferential "scores" of land use changes. The proposed spatial modeling approach is an exploratory data analysis, trying to discover useful pattems in spatial data that are not obvious to the data user and are useful in the spatial load forecast.