Short-Term Load Forecasting Using Semigroup Based System-Type Neural Network

This paper presents a methodology for short-term load forecasting using a semigroup-based system-type neural network. A technique referred to as algebraic decomposition is proposed for the neural network architecture, where the network is decomposed into a semigroup channel and a function channel. The semigroup channel, made of coefficient vector, is shown to exhibit the dependency of the load on temperature, and the function channel extracts the basis vector to represent the fundamental characteristics of daily load cycles. Regression and rearrangement methods are applied to handle the roughness of the load data surface, and interpolation and extrapolation of coefficient vector is achieved based upon the hourly temperature. The recombination of basis vector and coefficient vector at each hour gives the load forecast. This methodology is verified by testing on the load data from New England Independent System Operator (ISO) and achieves satisfactory results.