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.
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.
Original language | English |
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Title of host publication | IEEE International Energy Conference ENERGYCON 2018 |
Subtitle of host publication | Towards Self-healing, Resilient and Green Electric Power and Energy Systems |
Publisher | IEEE Institute of Electrical and Electronic Engineers |
Number of pages | 6 |
ISBN (Electronic) | 978-1-5386-3669-5 |
ISBN (Print) | 978-1-5386-1283-5 |
DOIs | |
Publication status | Published - 3 Jun 2018 |
MoE publication type | A4 Article in a conference publication |
Event | IEEE International Energy Conference, ENERGYCON 2018 - Limassol, Cyprus Duration: 3 Jun 2018 → 7 Jun 2018 http://www.energycon2018.org/ |
Conference
Conference | IEEE International Energy Conference, ENERGYCON 2018 |
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Abbreviated title | ENERGYCON 2018 |
Country/Territory | Cyprus |
City | Limassol |
Period | 3/06/18 → 7/06/18 |
Internet address |
Keywords
- forecasting
- machine learning
- physically based models
- hybrid models
- active demand
- optimization