Data-driven learning from dynamic pricing data - Classification and forecasting

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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Inspired by recent advances in data driven methods from deep-learning, this paper shows how neural networks can be trained to extract valuable information from smart meter data. We show how these methods can help provide new insight into the effectiveness of dynamic time of use pricing schemes. In addition we show how long-short term memory networks, a particular form of recurrent neural networks, allows including the information of dynamic prices to improve the accuracy of load forecasting. The renewables transition require flexibility sources to replace the regulation capability of traditional generation. Buildings have a large capacity to supply part of this flexibility by adjusting their consumption taking into account the needs of the energy systems. The use of time-of-use pricing is one of the simplest form of demand side management, but the effectiveness of such schemes are often hard to quantity. The smart meter roll-out is expected to help provide bring about new understanding of consumption patterns - but methods to analyse the data and extract the relevant information are needed. The energy domain is still relying on methods for data analysis that are time consuming, does not scale and require costly manual handling. The methods demonstrated learn from real data from a trial with dynamic time-of-use pricing in London, UK.

Original languageEnglish
Title of host publicationProceedings of 2019 IEEE Milan PowerTech
Number of pages6
PublisherIEEE
Publication date1 Jun 2019
Article number8810769
ISBN (Electronic)9781538647226
DOIs
Publication statusPublished - 1 Jun 2019
Event2019 IEEE Milan PowerTech, PowerTech 2019 - Milan, Italy
Duration: 23 Jun 201927 Jun 2019

Conference

Conference2019 IEEE Milan PowerTech, PowerTech 2019
CountryItaly
CityMilan
Period23/06/201927/06/2019
Series2019 IEEE Milan PowerTech, PowerTech 2019
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Classification, Dynamic time of use price, Feed forward neural network, Load forecasting, Long short-term memory, Recurrent neural network

ID: 194759395