Data-driven district energy management with surrogate models and deep reinforcement learning

Pinto, Giuseppe; Deltetto, Davide; Capozzoli, Alfonso

doi:10.1016/j.apenergy.2021.117642

Cited by 64 publications

(25 citation statements)

References 49 publications

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“…Afzalan and Jazizadeh [27] segment load data by user types to determine demand reduction potential, using a rough estimate of power reduction potential per degree change in thermostat set point to determine load shedding potential. Pinto et al [28] combine a surrogate modeling approach with machine learning techniques, developing a deep reinforcement learning controller that manages the operation of heat pumps and chilled and domestic hot water storage at the district scale, minimizing peak demand across the district while maintaining indoor temperatures within a comfort band for individual buildings. Chen et al [29] use a comfort temperature band to determine potential load flexibility in offices from thermostat adjustments and occupant temperature preferences, including the comfort band as an input to simple single zone energy balance equations; simple models of demand reduction potential from lighting and appliance and thermal storage are also reported.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

et al. 2022

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Section: Introductionmentioning

confidence: 99%

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

et al. 2022

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“…1) Dynamic modelling approaches of data centres A wide range of numerical models have been proposed for analysing building energy performance [16], [17]. For example, computational fluid dynamics (CFD) simulates air flow and temperature distribution in buildings, such as data centres, to study thermal behaviour [18].…”

Section: Research Gap and Main Contributionsmentioning

confidence: 99%

Assessment of Carbon-Aware Flexibility Measures From Data Centres Using Machine Learning

Misaghian

Tardioli

Cabrera

et al. 2023

IEEE Trans. on Ind. Applicat.

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Potential solutions for unlocking operational flexibility from data centres are investigated. Two types of data centres, in terms of their cooling facilities, are modelled in order to provide an in-depth assessment of potential variations in data centre performance based on their energy consumption and server temperatures. Using synthetic datasets generated from year-long building dynamic simulations under distinct system conditions, four machine learning (ML) models are designed and trained to predict data centre energy consumption and server temperatures. Three different ML algorithms are considered, including random forest, XGBOOST, and multiple linear regression, which all achieve high accuracy ranging from 93.8% to 98.1% for the mean absolute percentage error. The resulting ML models are then utilised to represent a system-wide fleet of data centres, which are integrated within a power system unit scheduling framework for potential demand shifting based on (system) carbon intensity. Five alternative flexibility scenarios are introduced and compared, with the objective of determining the achievable flexibility from a system-wide portfolio of data centres, focusing on concerns relating to server temperatures. On average, a 6.5% load reduction is seen to be achievable for a winter day during periods of high CO 2 intensity.

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“…Building Type Algorithm [152,153] Other/Mixed Cost Residential DQN [154] Cost and Load Balance [105] EV, ES, and RG Cost [155] Other [156] Cost and Comfort [157] HVAC, Fans, WH Cost [158] Other/Mixed Commercial [159] Cost and Comfort [160,161] EV, ES, and RG [175] Other/Mixed Cost [176] Cost and Comfort Residential TRPO Other/Mixed [182,183] Cost and Load Balance [184] Cost [185] EV, ES, and RG [186] Other/Mixed Cost and Comfort Academic [187] Other [188,189] EV, ES, and RG Commercial [190][191][192] HVAC, Fans, WH Cost and Comfort Mixed/NA [193][194][195] EV, ES, and RG Other [196,197] Other/Mixed Cost and Load Balance Residential SAC [198,199] HVAC, Fans, WH Cost Commercial [103,[200][201][202] Cost and Comfort [203] Other/Mixed [204] Academic [205][206][207] HVAC, Fans, WH Cost and Load Balance Mixed/NA [208][209][210]<...…”

Section: Reference Application Objectivementioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

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Data-driven district energy management with surrogate models and deep reinforcement learning

Cited by 64 publications

References 49 publications

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling

Assessment of Carbon-Aware Flexibility Measures From Data Centres Using Machine Learning

Reinforcement Learning: Theory and Applications in HEMS

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