2022/12/19 by Alona Zharova, Zharova, Alona, Antonia Scherz +1
Engineering · #Building Energy and Comfort Optimization #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Smart Grid Energy Management
paper · pdf · doi:10.48550/arxiv.2212.09426
openalex publication_date 2022/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
A well-performing prediction model is vital for a recommendation system suggesting actions for energy-efficient consumer behavior. However, reliable and accurate predictions depend on informative features and a suitable model design to perform well and robustly across different households and appliances. Moreover, customers' unjustifiably high expectations of accurate predictions may discourage them from using the system in the long term. In this paper, we design a three-step forecasting framework to assess predictability, engineering features, and deep learning architectures to forecast 24 hourly load values. First, our predictability analysis provides a tool for expectation management to cushion customers' anticipations. Second, we design several new weather-, time- and appliance-related parameters for the modeling procedure and test their contribution to the model's prediction performance. Third, we examine six deep learning techniques and compare them to tree- and support vector regression benchmarks. We develop a robust and accurate model for the appliance-level load prediction based on four datasets from four different regions (US, UK, Austria, and Canada) with an equal set of appliances. The empirical results show that cyclical encoding of time features and weather indicators alongside a long-short term memory (LSTM) model offer the optimal performance.