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Online Short-Term Forecast of System Heat Load in District Heating Networks
| Content Provider | Semantic Scholar |
|---|---|
| Author | Grosswindhager, Stefan Voigt, Andreas E. Kozek, Martin |
| Copyright Year | 2011 |
| Abstract | This paper presents an on-line short term forecasting appro ch for system heat load in district heating networks (DHN) using the popular Seasonal Autoregressive Integrate d Moving Average (SARIMA) models in state space representation. The system heat load itself is a non-statio nary random process composed of the individual consumer heat demands plus heat losses from pipes. Short term load for ecasting is essential for e ff ctive operational production planning. It was found that the recurring pattern of the proc ess based on half-hourly data are well described by a SARIMA(2,1,1)(0,1,1)48 model. The adequacy of the model w as confirmed by standard regression diagnostics. Furthermore, the identified SARIMA model was incorporated i nto the state space framework where classical Kalman Recursion allows convenient calculation of on-line foreca sting values. Moreover, exogenous e ffects such as weather effects are explicitly accounted for by decomposition of the or iginal time series into an outdoor temperature dependent part and a social component part, where the latter was again m odeled as SARIMA process. The relationship between system heat load and outdoor temperature may appropriately be expressed by a piece-wise linear function. Finally, the performance of the proposed model is validated on real da t by calculating the mean absolute percentage error (MAPE) value for 48-steps-ahead (24h) estimates. The on-li ne performance for the basic and the temperature adapted model was assessed by computing rolling 24-steps ahead MAPE values for approx. 20 days of real data. In this work, the Kalman procedure is presented as an elegant approa ch f r prediction of SARIMA processes in state space representation. Specifically, it is shown that the proposed m thods are suitable for on-line short term forecasting of system heat load in district heating networks. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://publik.tuwien.ac.at/files/PubDat_202018.pdf |
| Alternate Webpage(s) | http://publik.tuwien.ac.at/files/PubDat_202018.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
