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Improved modelling of electric loads for enabling demand response by applying physical and data-driven models: Project Response

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Details

Original languageEnglish
Title of host publication2018 IEEE International Energy Conference, ENERGYCON 2018
PublisherIEEE
Pages1-6
Number of pages6
ISBN (Electronic)9781538636695
DOIs
Publication statusPublished - 27 Jun 2018
Publication typeA4 Article in a conference publication
EventIEEE International Energy Conference - Limassol, Cyprus
Duration: 3 Jun 20187 Jun 2018

Conference

ConferenceIEEE International Energy Conference
CountryCyprus
CityLimassol
Period3/06/187/06/18

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.

Keywords

  • Active demand, Forecasting, Hybrid models, Machine learning, Optimization, Physically based models

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