Conference paper
Data-driven learning from dynamic pricing data - Classification and forecasting
Department of Electrical Engineering, Technical University of Denmark1
Energy Analytics and Markets, Center for Electric Power and Energy, Centers, Technical University of Denmark2
Acoustic Technology, Department of Electrical Engineering, Technical University of Denmark3
Technical University of Denmark4
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.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2019 |
| Pages: | 1-6 |
| Proceedings: | 2019 IEEE Milan PowerTech |
| Series: | 2019 Ieee Milan Powertech, Powertech 2019 |
| ISBN: | 1538647222 , 1538647230 , 9781538647226 and 9781538647233 |
| Types: | Conference paper |
| DOI: | 10.1109/PTC.2019.8810769 |
| ORCIDs: | Christensen, Morten Herget , Nozal, Diego Caviedes and Pinson, Pierre |
Classification Dynamic time of use price Feed forward neural network Load forecasting Long short-term memory Recurrent neural network SDG 7 - Affordable and Clean Energy
Feed Forward Neural Network Forecasting Logistics London Long Short-Term Memory Neural networks Pricing Recurrent Neural Network Smart meters UK data analysis data-driven learning deep-learning demand side management dynamic prices dynamic pricing data dynamic time-of-use pricing learning (artificial intelligence) load forecasting long-short term memory networks power engineering computing recurrent neural nets recurrent neural networks smart meter data smart meter roll-out smart meters use pricing schemes
