Details

Autor(en) / Beteiligte

• Desportes, Louis
• Andry, Pierre
• Fijalkow, Inbar
• David, Jérôme
• Kůrková, Věra
• Tetko, Igor V.
• Karpov, Pavel
• Theis, Fabian

Titel

Short-Term Temperature Forecasting on a Several Hours Horizon

Ist Teil von

• Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series, p.525-536

Ort / Verlag

Cham: Springer International Publishing

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Outside temperature is an important quantity in building control. It enables improvement in inhabitant energy consumption forecast or heating requirement prediction. However most previous works on outside temperature forecasting require either a lot of computation or a lot of different sensors. In this paper we try to forecast outside temperature at a multiple hour horizon knowing only the last 24 h of temperature and computed clear-sky irradiance up to the prediction horizon. We propose the use different neural networks to predict directly at each hour of the horizon instead of using forecast of one hour to predict the next. We show that the most precise one is using one dimensional convolutions, and that the error is distributed across the year. The biggest error factor we found being unknown cloudiness at the beginning of the day. Our findings suggest that the precision improvement seen is not due to trend accuracy improvement but only due to an improvement in precision.