ANN-Based City Heat Demand Forecast
Proceedings of the 12th IEEE PES PowerTech Conference towards and beyond Sustainable Energy Systems
2017
Kārlis Baltputnis,
Romāns Petričenko,
Antans Sauļus Sauhats
This paper discusses the importance of accurate forecasting tools in solving power system planning, modelling and optimization tasks. While artificial neural networks are widely considered to be one of the best prediction methods, their precision can vary greatly depending on the network structure and parameters. A method of experimentally finding the best ANN parameters has been offered and tested on heat demand forecasting. Some value of the benefits of increased prediction accuracy on the operation of CHP plants has been identified.
Keywords
Cogeneration, forecasting, market, optimization
DOI
10.1109/PTC.2017.7981097
Hyperlink
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7981097
Baltputnis, K., Petričenko, R., Sauhats, A. ANN-Based City Heat Demand Forecast. In: Proceedings of the 12th IEEE PES PowerTech Conference towards and beyond Sustainable Energy Systems, United Kingdom, Manchester, 18-22 June, 2017. Piscataway: IEEE, 2017, pp.1-6. ISBN 978-1-5090-4238-8. e-ISBN 978-1-5090-4237-1. Available from: doi:10.1109/PTC.2017.7981097
Publication language
English (en)