Air-Conditioning Load Forecasting for Prosumer Based on Meta Ensemble Learning

Abstract

Accurate and reliable prediction of air-conditioning load plays a significant role in prosumer energy management system (EMS), because air-conditioning load accounts for a large proportion of the building's total energy consumption. This paper proposes a new meta ensemble learning method to realize short-term prediction of air-conditioning load for prosumers. This method is a hybrid of meta ensemble learning and stacked auto-encoder (SAE). First, we design multiple different forecasting structures based on SAE to achieve point prediction of air-conditioning loads. SAE is used to learn the deep features in air-conditioning load data. Second, a new meta ensemble learning prediction model is proposed. Meta ensemble learning is used to learn the nonlinear features and invariant structures in data, and determine the coefficients of each SAE-based point forecaster. Finally, the prediction results of each point forecaster are aggregated and integrated to estimate the final air-conditioning load prediction result. Air-conditioning load data from a commercial building in Singapore are used to validate the feasibility and effectiveness of the proposed method, demonstrating that the proposed meta ensemble learning method is attractive in prosumer energy management.

Keywords

• Air-conditioning load
• load prediction
• meta learning
• ensemble learning
• prosumer energy management system