A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment

This paper presents a new approach to short-term load forecasting in a deregulated and price-sensitive environment. A real-time pricing type scenario is envisioned where energy prices could change on an hourly basis with the consumer having the ability to react to the price signal through shifting his electricity usage from expensive hours to other times when possible. The load profile under this scenario would have different characteristics compared to that of the regulated, fixed-price era. Consequently, short-term load forecasting models customized on price-insensitive (PIS) historical data of regulated era would no longer be able to perform well. In this work, a price-sensitive (PS) load forecaster is developed. This forecaster consists of two stages, an artificial neural network based PIS load forecaster followed by a fuzzy logic (FL) system that transforms the PIS load forecasts of the first stage into PS forecasts. The first stage forecaster is a widely used forecaster in industry known as ANNSTLF. For the FL system of the second stage, a genetic algorithm based approach is developed to automatically optimize the number of rules and the number and parameters of the fuzzy membership functions. Another FL system is developed to simulate PS load data from the PIS historical data of a utility. This new forecaster termed NFSTLF is tested on three PS database and it is shown that it produces superior results to the PIS ANNSTLF.