CityLearn

This output is listed in the Digital Public Goods Alliance Registry and contributes to the following UN Sustainable Development Goals (SDGs):

CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities [10, 25, 27]. A major challenge for RL in demand response is the ability to compare algorithm performance [29]. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.

Districts and cities have periods of high demand for electricity, which raise electricity prices and the overall cost of the power distribution networks. Flattening, smoothening, and reducing the overall curve of electrical demand helps reduce operational and capital costs of electricity generation, transmission, and distribution networks. CityLearn allows the easy implementation of reinforcement learning agents in a single or multi-agent setting to reshape their aggregated curve of electrical demand by controlling active energy storage for load shifting and heat pump or electric heater power for load shedding.

Applications

CityLearn has been utilized in the following projects and publications:

Application	Examples
Benchmarking reinforcement learning algorithms	Dhamankar et al. [4], Khayatian et al. [7], Nweye et al. [12], Pinto et al. [19], Qin et al. [21]
Coordinated energy management	Deltetto [2], Glatt et al. [5], Kathirgamanathan et al. [6], Pinto et al. [18], Pinto et al. [20], Qin et al. [22], Vazquez-Canteli et al. [28], Yang et al. [30]
Incentive based demand-response	Deltetto et al. [3], Tolovski [23]
Independent energy management	Chen et al. [1], Nweye et al. [13]
Meta-learning	Zhang et al. [31]
Model predictive control	Zhan, Sicheng et al. [32]
Transfer learning	Nweye et al. [14]
Voltage regulation	Pigott et al. [17]

Cite CityLearn

@article{doi:10.1080/19401493.2024.2418813,
   author = {Nweye, Kingsley and Kaspar, Kathryn and Buscemi, Giacomo and Fonseca, Tiago and Pinto, Giuseppe and Ghose, Dipanjan and Duddukuru, Satvik and Pratapa, Pavani and Li, Han and Mohammadi, Javad and Lino Ferreira, Luis and Hong, Tianzhen and Ouf, Mohamed and Capozzoli, Alfonso and Nagy, Zoltan},
   title = {CityLearn v2: energy-flexible, resilient, occupant-centric, and carbon-aware management of grid-interactive communities},
   journal = {Journal of Building Performance Simulation},
   volume = {0},
   number = {0},
   pages = {1--22},
   year = {2024},
   publisher = {Taylor \& Francis},
   doi = {10.1080/19401493.2024.2418813},
   url = {https://doi.org/10.1080/19401493.2024.2418813},
}

Indices and tables

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• Module Index

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