Meta-learning in multivariate load demand forecasting with exogenous meta-features

Although many studies have examined various types of single load demand prediction algorithms, it is yet a challenging decision to select the best predictor. Geographical characteristics are one of the most effective factors in load consumption patterns, the performance of the algorithms, and the accuracy of predictions. Therefore, developing a general framework in which meteorological characteristics determine the best predictor can be of importance. Meta-learning models, which are less considered in this area, propose a framework for algorithm selection by using a set of meta-features. In this paper, a meta-learning system is developed using exogenous weather variables as meta-features. The proposed system selects the best predictor among a pool of candidate forecasting algorithms including ARIMA, ARIMAX, MLR, NAR, NARX, and SVR. In statistical approaches, the volatility of the mean and variance of the load demand is modeled by GARCH process. The selection process uses a set of meta-features built on temperature and dew point. The empirical tests were performed on 30 samples of daily load demand taken from ten various geographical zones in Ontario, Canada. The achievements reveal that the characteristics of weather meta-features such as range of variation, skewness, and kurtosis play an important role in assigning the most suitable predictor to each sample set and both groups of statistical and artificial intelligence-based predictors were selected in different sample sets.