Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | 2018 IEEE International Energy Conference, ENERGYCON 2018 |
| Publisher | IEEE |
| Pages | 1-6 |
| Number of pages | 6 |
| ISBN (Electronic) | 9781538636695 |
| DOIs | |
| Publication status | Published - 27 Jun 2018 |
| Publication type | A4 Article in a conference publication |
| Event | IEEE International Energy Conference - Limassol, Cyprus Duration: 3 Jun 2018 → 7 Jun 2018 |
Conference
| Conference | IEEE International Energy Conference |
|---|---|
| Country | Cyprus |
| City | Limassol |
| Period | 3/06/18 → 7/06/18 |
Abstract
Accurate load and response forecasts are a critical enabler for high demand response penetrations and optimization of responses and market actions. Project RESPONSE studies and develops methods to improve the forecasts. Its objectives are to improve 1) load and response forecast and optimization models based on both data-driven and physical modelling, and their hybrid models, 2) utilization of various data sources such as smart metering data, weather data, measurements from substations etc., and 3) performance criteria of load forecasting. The project applies, develops, compares, and integrates various modelling approaches including partly physical models, machine learning, modern load profiling, autoregressive models, and Kalman-filtering. It also applies non-linear constrained optimization to load responses. This paper gives an overview of the project and the results achieved so far.
ASJC Scopus subject areas
Keywords
- Active demand, Forecasting, Hybrid models, Machine learning, Optimization, Physically based models