This paper presents an artificial neural network(ANN) approach t o electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead forecasts in our test on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.

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A theoretical framework to analyze the effects of using on-line optimization in the feedback loop is developed and applied to obtain a suboptimal controller guaranteed to yield asymptotically stable systems.Key Words--Computer control; constrained systems; dynamic programming; feedback control; on-line operation; optimization; stability; suboptimal control; trees.Abstract--Recent advances in computer technology have spurred new interest in the use of feedback controllers based upon on-line minimization for the control of constrained linear systems. Still the use of computers in the feedback loop has been hampered by the fact that the amount of time available for computation in most sampled data systems is not enough to achieve a complete solution using conventional algorithms. Several "ad hoc" techniques have been proposed, but their applicability is restricted by the lack of supporting theory. In this paper we present a theoretical framework to analyze the stability of the closed-loop system resulting from the use of on-line optimization in the feedback loop. Using these results we show that a suboptimal algorithm, based upon the use of heuristic search techniques, yields asymptotically stable systems, provided that enough computation power is available to solve at each sampling interval an optimization problem considerably simpler than the original. The controller presented in this paper is valuable for situations where the customary approaches of using Pontryagin's minimum principle or storing a family of extremal curves are not applicable due to limitations in the computational resources available.

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