Improved modelling of electric loads for enabling demand response by applying physical and data-driven models : Project Response
Koponen, Pekka; Hanninen, Seppo; Mutanen, Antti; Koskela, Juha; Rautiainen, Antti; Järventausta, Pertti; Niska, Harri; Kolehmainen, Mikko; Koivisto, Hannu (2018-06-27)
Koponen, Pekka
Hanninen, Seppo
Mutanen, Antti
Koskela, Juha
Rautiainen, Antti
Järventausta, Pertti
Niska, Harri
Kolehmainen, Mikko
Koivisto, Hannu
IEEE
27.06.2018
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201808172164
https://urn.fi/URN:NBN:fi:tty-201808172164
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [16726]