A machine learning pipeline for demand response capacity scheduling

Gautham Krishnadas, Aristides Kiprakis

Research output: Contribution to journalArticlepeer-review

Abstract

Demand response (DR) is an integral component of the smart grid operations that offers the required flexibility to support its decarbonisation. In incentive-based DR programs, deviations from the scheduled DR capacity affect the grid’s energy balance and result in revenue losses for the DR participants. This issue aggravates with the increasing interest in DR from participants such as large consumer buildings who have limited standard methods to follow for DR capacity scheduling. Load curtailment based DR capacity availability from such consumers can be forecasted with the help of supervised machine learning (ML) models. This study demonstrates the development of data-driven ML based total and flexible load forecast models for a retail building. The ML model development tasks such as data pre-processing, training-testing dataset preparation, cross-validation, algorithm selection, hyperparameter optimisation, feature ranking, model selection and model evaluation are guided by deployment-centric design criteria such as reliability, computational-efficiency and scalability. Based on the selected performance metrics, the day-ahead and week-ahead ML based load forecast models developed for the retail building are shown to out-perform the synthesised naïve models. Further, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of workflow management systems, computational resources, monitoring tools and visualisation dashboards. The ML pipeline ensures faster and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourage increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.
Original languageEnglish
Article number1848
Number of pages25
JournalEnergies
Volume13
Issue number7
DOIs
Publication statusPublished - 10 Apr 2020

Keywords

  • Machine Learning
  • Data-driven
  • smart grid
  • Demand response
  • Load modelling

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