Electric vehicles play a crucial role in the global transition towards sustainability, particularly highlighted in initiatives like the European Green Deal. With projections indicating a significant increase in electric vehicle adoption worldwide, including a notable surge in the EU and Sweden, the strain on existing electric infrastructure becomes a concern. Managed charging – the process of regulating the charging of electric vehicles in a coordinated manner – emergesas a promising strategy to mitigate this strain, optimizing charging schedules to alleviate peakloads, and reduce the need for extensive grid upgrades. However, naive peak shaving approaches may fall short in addressing systemic issues, prompting the need for smarter solutions based on predictive modelling. This thesis focuses on Dansmästaren, a parking garage designed for mass electric vehicle charging, located in Uppsala, Sweden. Through load shifting techniques, one approach being explored at Dansmästaren aims to avoid grid capacity constraints by strategically scheduling EV charging to off-peak hours. This is being done using smart charging, which utilizes predictive models to predict charging durations for the scheduling of EV charging. This thesis aims to aid such predictive models by constructing a new feature for these models totrain on, namely clusters. These clusters are created using time series clustering, a technique that groups time series to clusters by running a range of algorithms comparing the similarity of different time series to each other using a variety of distance measures. In this case, the study uses data collected during three months in the form of time series, split by charging sessions, to construct the clusters. The performance of these clusters are then tested using deep learning as a predictive model to evaluate whether or not, and to which degree, the construction of clusters helped the predictive model achieve better results. Different approaches and algorithms are tested and evaluated for the time series clustering with the intention of getting the best possible performance — here meaning the specific construction of clusters resulting in the best performance increase for overall predictions. Different approaches were also tested and evaluated for the deep learning model, although not to the same extent, since the time series clustering is the focus of this thesis. In the end, a predictive performance increase of up to 17% was achieved by the predictive model using the constructed clusters as an additional feature. This suggests that time series clustering can aid deep learning models better predict charging durations.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-533219 |
| Date | January 2024 |
| Creators | Palmlöf, Otto |
| Publisher | Uppsala universitet, Elektricitetslära |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | UPTEC STS, 1650-8319 ; 24014 |
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