Short-Term Forecasting of District Heating Demand

2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe 2018) 2018
Romāns Petričenko, Dmitrijs Soboļevskis, Antans Sauļus Sauhats

Focus of the paper is statistical data pre-processing before it applies for prediction the thermal load in district heating networks, focusing on day-ahead hourly planning. Such a planning is highly important for cogeneration plants participating in electricity wholesale markets. Article considers the possibility of correcting detected inconsistencies into district heating statistical data using forecasted values of the heat demand. The case study is based on the examples of heat supply of a large city, gas fired cogeneration power plants and real world data. The cost of errors in the prediction of heat consumption is estimated.

Keywords
energy market, district heating, cogeneration, forecasting, error analysis
DOI
10.1109/EEEIC.2018.8494362
Hyperlink
https://ieeexplore.ieee.org/document/8494362

Petričenko, R., Soboļevskis, D., Sauhats, A. Short-Term Forecasting of District Heating Demand. In: 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe 2018), Italy, Palermo, 12-15 June, 2018. Piscataway, NJ: IEEE, 2018, pp.807-812. ISBN 978-1-5386-5187-2. e-ISBN 978-1-5386-5186-5. Available from: doi:10.1109/EEEIC.2018.8494362

Publication language
English (en)