Machine Learning Point Forecasts Methods

Abstract

The traditional statistical and benchmark methods presented in Sect. 9.1 often assume some relatively simple relationship between the dependent and independent variables, be that linear trends, particular seasonalities or autoregressive behaviours. They have performed quite successfully for load forecasting, being quite accurate, even with low amounts of data, and can easily be interpreted by practitioners. However, the methods described in Sect. 9.1 may be less suitable for modelling more complex and highly nonlinear relationships. As data has become more ubiquitous due to increased monitoring, machine learning methods are becoming increasingly common as they can find complicated and subtle patterns in the data.