Forecasting Time Series for Electricity Consumption Data Using Dynamic Weight Ensemble Model

Abstract

Electricity load forecasting is a prevalent research topic in recent years. In this study, we predict the electricity consumption using only previous power data (i.e., without using weather information or other features). We survey existing univariate methods such as MLP-based, CNN-based, XGBoost-based, RF-based, and EN3-bestK. However, these existing methods do not perform well due to that the range of power values varies a lot. Therefore, we present an electricity consumption forecast system called Dynamic Weight Ensemble Model (DWEM). There are three stages in the proposed DWEM. First of all, we provide three types of data serialization in data preprocessing. Second, we train four types of models (i.e., MLP-based, CNN-based, XGBoost-based, and RF-based) for building the ensemble model later. Finally, we combine the four types of models into an ensemble model, using the proposed Two-Phase Ensemble. In the two-phase ensemble, the first phase is to ensemble the models trained using the same algorithm but different serializations, and the second phase is to ensemble the models from different algorithms. The two-phase ensemble method is designed to dynamically adjust weights based on the previous performance of the corresponding models. Moreover, we notice that properly handling missing values is an important factor in system performance. Therefore, we present a statistical method to estimate the missing values. We compare DWEM with various state-of-the-art methods. Comparison of DWEM and the state-of-the-art ensemble method, the results show that DWEM is on average about 46.95% and 44.47% better than EN3-bestK on the MAPE and MAE indicators, respectively.