A data-driven approach for steam load prediction in buildings

• Published in: Applied energy Vol. 87; no. 3; pp. 925 - 933
• Main Authors: Kusiak, Andrew, Li, Mingyang, Zhang, Zijun
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.03.2010; Elsevier
• Series: Applied Energy
• Subjects: Applied sciences; Building load estimation; Data mining; Data mining Building load estimation Steam load prediction Neural network ensemble Energy forecasting Monte Carlo simulation Parameter selection; Economic data; Energy; Energy economics; Energy forecasting; Energy policy; Exact sciences and technology; General, economic and professional studies; Methodology. Modelling; Monte Carlo simulation; Neural network ensemble; Parameter selection; Steam load prediction; Steam load prediction; Parameter selection; Neural network ensemble; Data mining; Building load estimation; Energy forecasting; Monte Carlo simulation; Energy consumption; Monte Carlo method; Steam heating; Uncertainty; Cooling; Buildings; Climatic data; Neural network; Algorithm; Forecasting; Simulation; Energy management

Predicting building energy load is important in energy management. This load is often the result of steam heating and cooling of buildings. In this paper, a data-driven approach for the development of a daily steam load model is presented. Data-mining algorithms are used to select significant parame...