Pattern similarity-based machine learning methods for mid-term load forecasting: A comparative study

Pattern similarity-based frameworks are widely used for classification and regression problems. Repeated, similar-shaped cycles observed in seasonal time series encourage the use of such frameworks for forecasting. In this paper, we use pattern similarity-based models for mid-term load forecasting. An integral part of these models is the use of patterns of time series sequences for time series representation. Pattern representation ensures input and output data unification through trend filtering and variance equalization. This simplifies the forecasting problem and allows us to use models based on pattern similarity. We consider four such models: nearest-neighbor model, fuzzy neighborhood model, kernel regression model, and general regression neural network. Three variants of the approach were proposed. A basic one and two hybrid solutions combining similarity-based and statistical methods (ARIMA and exponential smoothing). In the experimental part of the work, the proposed models were used to forecast the monthly electricity demand in 35 European countries. The results show the high performance of the proposed models, which outperform both the comparative classical statistical models and machine learning models in terms of accuracy, simplicity, and ease of optimization. Among the proposed variants, a hybrid approach combining similarity-based methods with exponential smoothing turned out to be the most accurate. The study highlights the many advantages of the proposed pattern similarity based models such as clear operation principles, a small number of parameters to adjust, no training procedure, fast optimization procedure, good generalization ability, ability to work on the newest data without retraining, and delivery of multi-step forecasts. (c) 2021 Elsevier B.V. All rights reserved.