This reconfiguration algorithm starts with all operable switches open, and at each step, closes the switch that results in the least increase in the objective function. The objective function is defined as incremental losses divided by incremental load served. A simplified loss formula is used to screen candidate switches, but a full load flow after each actual switch closing maintains accurate loss and constraint information. A backtracking option mitigates the algorithm's greedy search. This algorithm takes more computer time than other methods, but it models constraints and control action more accurately. A network load flow is used to provide a lower bound on the losses. The paper includes results on several test systems used by other authors.

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This paper describes the application of a phase-space embedding concept to artificial neural network ( A m ) based short-term electric load forecasting. Embedding parmeters for electric load time-series were determined using the method of Integral Local Deformation. In the reconstructed phase-space modular A " predictor was trained to predict loads up to jive days ahead in one-hour steps. It was found that addition of temperature and cycle variables to the phase-space based input variable set improved forecasting accuracy. The overall number of input variables was much smaller than in the similar cases reported in the literature. In this manner the sue of historical data set needed for training was SignificantIy reduced. Forecasting errors were comparable to or better than the ones reported for the similar cases. Such characteristics make the approach attractive for shortterm load forecasting in the deregulated electric power industy.

This paper addresses short-term electric load forecasting using machine learning and neural network techniques. Neural networks, though accurate in weekday load forecasting, are poor at forecasting maximum daily load, weekend and holiday loads. This necessitates development of a robust forecasting technique to complement the neural networks for enhanced reliability of forecast and improved overall accuracy. The statistical decision tree method produces robust forecasts and human intelligible rules. These rules provide understanding of factors driving load demand. Decision trees when combined with neural network forecasts, produce robust and accurate forecasts. Simulations are performed on a service area susceptible to large and sudden changes in weather and load. Forecasts obtained by the proposed method are accurate under diverse conditions.

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