A Machine Learning Pipeline for Demand Response Capacity Scheduling
Demand response (DR) is an integral component of smart grid operations that offers the necessary 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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/7/1848 |
| _version_ | 1797570989831225344 |
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| author | Gautham Krishnadas Aristides Kiprakis |
| author_facet | Gautham Krishnadas Aristides Kiprakis |
| author_sort | Gautham Krishnadas |
| collection | DOAJ |
| description | Demand response (DR) is an integral component of smart grid operations that offers the necessary 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 increasing DR delivery 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 reliably 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 outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid. |
| first_indexed | 2024-03-10T20:33:12Z |
| format | Article |
| id | doaj.art-ae640c2739a549e2a2db8b7002b1c8d2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T20:33:12Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ae640c2739a549e2a2db8b7002b1c8d22023-11-19T21:14:43ZengMDPI AGEnergies1996-10732020-04-01137184810.3390/en13071848A Machine Learning Pipeline for Demand Response Capacity SchedulingGautham Krishnadas0Aristides Kiprakis1Flexitricity Limited, Edinburgh EH3 9DN, UKInstitute for Energy Systems, School of Engineering, University of Edinburgh, Edinburgh EH8 9YL, UKDemand response (DR) is an integral component of smart grid operations that offers the necessary 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 increasing DR delivery 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 reliably 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 outperform the timeseries persistence models used for benchmarking. Furthermore, the deployment of these models for DR capacity scheduling is proposed as an ML pipeline that can be realised with the help of ML workflows, computational resources as well as systems for monitoring and visualisation. The ML pipeline ensures faster, cost-effective and large-scale deployment of forecast models that support reliable DR capacity scheduling without affecting the grid’s energy balance. Minimisation of revenue losses encourages increased DR participation from large consumer buildings, ensuring further flexibility in the smart grid.https://www.mdpi.com/1996-1073/13/7/1848machine learningdata-drivendeploymentsmart griddemand responseflexibility |
| spellingShingle | Gautham Krishnadas Aristides Kiprakis A Machine Learning Pipeline for Demand Response Capacity Scheduling Energies machine learning data-driven deployment smart grid demand response flexibility |
| title | A Machine Learning Pipeline for Demand Response Capacity Scheduling |
| title_full | A Machine Learning Pipeline for Demand Response Capacity Scheduling |
| title_fullStr | A Machine Learning Pipeline for Demand Response Capacity Scheduling |
| title_full_unstemmed | A Machine Learning Pipeline for Demand Response Capacity Scheduling |
| title_short | A Machine Learning Pipeline for Demand Response Capacity Scheduling |
| title_sort | machine learning pipeline for demand response capacity scheduling |
| topic | machine learning data-driven deployment smart grid demand response flexibility |
| url | https://www.mdpi.com/1996-1073/13/7/1848 |
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