Improving Individual-level Short-Term Electric Load Forecasting for Residential Demand Response

Abstract

Forecasting electric load is one of critical challenges in smart grid, and it can be particularly challenging for residential demand response where each households daily electricity load can vary randomly and significantly. For improving forecasting accuracy to calculate reduction amount based on predicted load in demand response program, a general and widely accepted enhancement method is to set up an independent control group, but it requires a careful selection process and exclusion of the selected customers. The next best plan is to develop a forecasting method itself. To predict each customer's load accurately, the general belief is that the best way to predict electric load is through individualized models. The existing studies, however, have focused on one-for-all models because the individual models are difficult to train and require a significantly larger data size per individual. In recent years, applying deep learning for forecasting electric load has become an important research topic, but still one-for-all has been the main approach. In this paper, we propose three methods. First, virtual control group adjustment method can provide the benefits of control group without requiring the main burdens for the first problem. Using a real-world dataset collected from a pilot residential demand response program, we evaluate the robustness of virtual control group adjustment method against selection bias and assess the suggested adjustment methods mean error performance and mean absolute error performance against the traditional models. Secondly, we adopt transfer learning and meta learning that can be smoothly integrated into deep neural networks, and show how a high-performance individualized model can be formed using the individual's data collected over just several days. This is made possible by extracting the common patterns of many individuals using a sufficiently large dataset and then customizing each individual model using the specific individual's small dataset. The proposed methods are evaluated over residential and non-residential datasets. Finally, we suggest unified methods that combine the suggested adjustment method and the suggested individualization methods. Unified methods performed best in our experiment. We also show that our proposed methods using meta learning could be used as effective tools when there is a cold-start problem.