Publications• Sorted by Date • Classified by Publication Type • Classified by Research Category • Energy- and Cost-Efficient Pumping Station ControlTimon V. Kanters, Frans A. Oliehoek, Michael Kaisers, Stan R. van den Bosch, Joep Grispen, and Jeroen Hermans. Energy- and Cost-Efficient Pumping Station Control. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp. 3842–3848, February 2016. DownloadAbstractWith renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained. BibTeX Entry@inproceedings{Kanters16AAAI, author = {Timon V. Kanters and Frans A. Oliehoek and Michael Kaisers and Stan R. van den Bosch and Joep Grispen and Jeroen Hermans}, title = {Energy- and Cost-Efficient Pumping Station Control}, booktitle = AAAI16, year = 2016, month = feb, pages = {3842--3848}, url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12035/12169}, abstract = { With renewable energy becoming more common, energy prices fluctuate more depending on environmental factors such as the weather. Consuming energy without taking volatile prices into consideration can not only become expensive, but may also increase the peak load, which requires energy providers to generate additional energy using less environment-friendly methods. In the Netherlands, pumping stations that maintain the water levels of polder canals are large energy consumers, but the controller software currently used in the industry does not take real-time energy availability into account. We investigate if existing AI planning techniques have the potential to improve upon the current solutions. In particular, we propose a light weight but realistic simulator and investigate if an online planning method (UCT) can utilise this simulator to improve the cost-efficiency of pumping station control policies. An empirical comparison with the current control algorithms indicates that substantial cost, and thus peak load, reduction can be attained. } }
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