Abstract
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.
The research reported in this publication is part of the EcobioH2 project supported by EcoBio and ADEME, the french agency for environnement and energy. This project is funded by the PIA, the french national investment plan for innovation.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
International Codes, Volume I.1, Annex II to the WMO Technical Regulations: part A- Alphanumeric Codes (2011–2018). https://library.wmo.int/doc_num.php?explnum_id=5708
Meteorological Service for International Air Navigation (Annex 3) (2013). https://www.icao.int/Meetings/METDIV14/Documents/an03_cons_secured.pdf
ECOBIO H2 – ADEME, March 2019. https://www.ademe.fr/ecobio-h2
Ecobioh2 - etis, February 2019. https://ecobioh2.ensea.fr
HelioClim-3 Archives for Free - www.soda-pro.com. March 2019. http://www.soda-pro.com/web-services/radiation/helioclim-3-archives-for-free. Accessed 11 Mar 2019
Abdel-Aal, R.: Hourly temperature forecasting using abductive networks. Eng. Appl. Artif. Intell. 17(5), 543–556 (2004). https://doi.org/10.1016/j.engappai.2004.04.002
Abhishek, K., Singh, M., Ghosh, S., Anand, A.: Weather forecasting model using artificial neural network. Procedia Technol. 4, 311–318 (2012). https://doi.org/10.1016/j.protcy.2012.05.047. 2012 C3IT
Andreas, A.M.: NREL: Measurement and Instrumentation Data Center (MIDC), March 2019. https://midcdmz.nrel.gov. Accessed 2 Apr 2019
Deihimi, A., Orang, O., Showkati, H.: Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57, 382–401 (2013). https://doi.org/10.1016/j.energy.2013.06.007
Gelaro, R., McCarty, W., Suárez, M.J., Todling, R., et al.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Clim., June 2017. https://doi.org/10.1175/JCLI-D-16-0758.1
Hayati, M., Mohebi, Z.: Application of artificial neural networks for temperature forecasting. Int. J. Elect. Comput. Energ. Electron. Commun. Eng. 1(4), 662–666 (2007). https://doi.org/10.5281/zenodo.1070987
Ineichen, P.: Quatre années de mesures d’ensoleillement à Genève. Ph.D. thesis 19 July 1983. https://doi.org/10.13097/archive-ouverte/unige:17467
Korzeniowski, F., Widmer, G.: A fully convolutional deep auditory model for musical chord recognition. In: 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), pp. 1–6, Septembe 2016. https://doi.org/10.1109/MLSP.2016.7738895
LeCun, Y., Bengio, Y., et al.: Convolutional networks for images, speech, and time series. Handb. Brain Theor. Neural Netw. 3361(10) (1995)
Ramakrishna, R., Bernstein, A., Dall’Anese, E., Scaglione, A.: Joint probabilistic forecasts of temperature and solar irradiance. In: IEEE ICASSP 2018. https://doi.org/10.1109/ICASSP.2018.8462496
Salque, T., Marchio, D., Riederer, P.: Neural predictive control for single-speed ground source heat pumps connected to a floor heating system for typical french dwelling. Building Serv. Eng. Res. Technol. 35(2), 182–197 (2014). https://doi.org/10.1177/0143624413480370
Salque, T.: Méthode d’évaluation des performances annuelles d’un régulateur prédictif de PAC géothermiques sur banc d’essai semi-virtuel. Ph.D. thesis (2013), http://www.theses.fr/2013ENMP0095, eNMP 2013
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Desportes, L., Andry, P., Fijalkow, I., David, J. (2019). Short-Term Temperature Forecasting on a Several Hours Horizon. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series. ICANN 2019. Lecture Notes in Computer Science(), vol 11730. Springer, Cham. https://doi.org/10.1007/978-3-030-30490-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-030-30490-4_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30489-8
Online ISBN: 978-3-030-30490-4
eBook Packages: Computer ScienceComputer Science (R0)