A Flexible Mixed Additive-Multiplicative Model for Load Forecasting in a Smart Grid Setting

This paper presents a mixed additive-multiplicative model for load forecasting that can be flexibly adapted to accommodate various forecasting needs in a Smart Grid setting. The flexibility of the model allows forecasting the load at different levels: system level, transform substation level, and feeder level. It also enables us to conduct short-term, medium and long-term load forecasting. The model decomposes load into two additive parts. One is independent of weather but dependent on the day of the week (d) and hour of the day (h), denoted as $$L:0(d,h)$$. The other is the product of a weather-independent normal load, $$L:1(d,h)$$, and weather-dependent factor, f(w). Weather information (w) includes the ambient temperature, relative humidity and their lagged versions. This method has been evaluated on real data for system level, transformer level and feeder level in the Northeastern part of the USA. Unlike many other forecasting methods, this method does not suffer from the accumulation and propagation of errors from prior hours.