2018/08/02 by Jiyang Xie, Zhanyu Ma, Xie, Jiyang +3
Computer Science · Engineering · Mathematics · #Building Energy and Comfort Optimization #Energy Load and Power Forecasting #FOS: Computer and information sciences #Integrated Energy Systems Optimization #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1808.00961
Technical Report
openalex publication_date 2018/08/02 · arxiv created 2020/01/28 · arxiv updated 2020/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to understand the influence of different parameters. Based on an Elman neural network (ENN), this paper investigates the impact of direct solar irradiance and wind speed on predicting the heat demand of a district heating network. Results show that including wind speed can generally result in a lower overall mean absolute percentage error (MAPE) (6.43%) than including direct solar irradiance (6.47%); while including direct solar irradiance can achieve a lower maximum absolute deviation (71.8%) than including wind speed (81.53%). In addition, even though including both wind speed and direct solar irradiance shows the best overall performance (MAPE=6.35%).