The rollout of smart meters and steadily increasing sample rates lead to a growing amount of raw data available for short-term load forecasting (STLF). While the original motivation for high resolutions has been the enabling of non-intrusive load monitoring (NILM), so far their value for STLF has been limited. We propose a novel approach, which allows the exploitation of high resolution data for STLF, by incorporating NILM and subsequent clustering of similarly behaving appliances as a preprocessing step.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Din GMU, Marnerides AK (2017) Short term power load forecasting using Deep Neural Networks. In: 2017 International conference on computing, networking and communications (ICNC). IEEE, pp 594–598
Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891. doi:10.1109/5.192069
Kelly J, Knottenbelt W (2015) Neural nilm: deep neural networks applied to energy disaggregation. In: Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments. ACM, pp 55–64
Srivastava AK, Pandey AS, Singh D (2016) Short-term load forecasting methods: a review. In: International conference on emerging trends in electrical electronics & sustainable energy systems (ICETEESES). IEEE, pp 130–138
Wijaya TK, Humeau S, Vasirani M, Aberer K (2014) Residential electricity load forecasting: evaluation of individual and aggregate forecasts. Technical report, Citeseer
About this article
Cite this article
Wurm, M., Coroamă, V.C. Poster abstract: grid-level short-term load forecasting based on disaggregated smart meter data. Comput Sci Res Dev 33, 265–266 (2018). https://doi.org/10.1007/s00450-017-0374-3
- Smart grid