Heat demand forecasting algorithm for a Warsaw district heating network
| Autor: | Konrad Świrski, Konrad Wojdan, Rafał Brzozowski, Rafał Serafin, Artur Bielecki, Teresa Kurek, Michal Guzek, Jakub Bialek |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Meteorology 020209 energy Mechanical Engineering Linear model 02 engineering and technology Building and Construction Demand forecasting Grid Pollution Fuzzy logic Industrial and Manufacturing Engineering Variable (computer science) General Energy 020401 chemical engineering Autoregressive model 0202 electrical engineering electronic engineering information engineering Environmental science media_common.cataloged_instance 0204 chemical engineering Electrical and Electronic Engineering European union Civil and Structural Engineering media_common |
| Zdroj: | Energy. 217:119347 |
| ISSN: | 0360-5442 |
| Popis: | This paper presents a complex analysis of heat demand forecasting methods for the Warsaw District Heating Network, which is owned by Veolia Energia Warszawa, the largest district heating network (DHN) in the European Union. The analyzed network supplies heat for both domestic and heating purposes. Therefore, summer, intermediate, and winter seasons were delineated and separately evaluated. Numerous models were utilized including models broadly recognized and used (ridge regression, autoregression with exogenous input, deep artificial neural networks), as well as previously unexplored models (combination of summer and winter linear models with the utilization of fuzzy logic). A 72 h forecast horizon is evaluated for total heat demand (the sum of all substations), as well as for groups of buildings (local models for specific city areas), and individually for the majority of substations. Models of areas use an additional input variable, namely, the results of the total heat demand forecast, and are proposed to be developed as an auxiliary information variable offered to grid operators. An artificial neural network based model achieves the best accuracy for all analyzed seasons. The intermediate seasons prove to be the most difficult to accurately forecast for and only the combination of summer and winter linear autoregresive models with utilization of a fuzzy logic reached comparable accuracy. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect