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| Content Provider | ACM Digital Library |
|---|---|
| Author | Saguna, Saguna Mitra, Karan Nanda, Rohan Åhlund, Christer |
| Abstract | Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behaviour, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model show that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour. |
| Starting Page | 2135 |
| Ending Page | 2141 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781450337397 |
| DOI | 10.1145/2851613.2853127 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-04-04 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | District heating system Machine learning Bayesian network Forecasting Smart grid |
| Content Type | Text |
| Resource Type | Article |
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