Data-driven based HVAC optimisation approaches: A Systematic Literature Review

M Ala'raj, M Radi, MF Abbod, M Majdalawieh… - Journal of Building …, 2022 - Elsevier
Improving the energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC)
systems is crucial to reduce buildings' energy costs and their carbon footprint. HVAC …

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

F Li, Y Du - Deep Learning for Power System Applications: Case …, 2023 - Springer
In this chapter, a novel data-driven method, which is called the deep deterministic policy
gradient (DDPG), is applied for optimally controlling the multi-zone residential heating …

[HTML][HTML] Reinforcement learning for whole-building HVAC control and demand response

D Azuatalam, WL Lee, F De Nijs, A Liebman - Energy and AI, 2020 - Elsevier
This paper proposes a novel reinforcement learning (RL) architecture for the efficient
scheduling and control of the heating, ventilation and air conditioning (HVAC) system in a …

A home energy management system with an integrated smart thermostat for demand response in smart grids

AC Duman, HS Erden, Ö Gönül, Ö Güler - Sustainable Cities and Society, 2021 - Elsevier
Smart thermostats and home energy management systems (HEMSs) are generally studied
separately. However, their joint use can provide a greater benefit. Therefore, this study …

Optimal integration of a hybrid solar-battery power source into smart home nanogrid with plug-in electric vehicle

X Wu, X Hu, Y Teng, S Qian, R Cheng - Journal of power sources, 2017 - Elsevier
Hybrid solar-battery power source is essential in the nexus of plug-in electric vehicle (PEV),
renewables, and smart building. This paper devises an optimization framework for efficient …

Optimal energy management system for microgrids considering energy storage, demand response and renewable power generation

AK Erenoğlu, İ Şengör, O Erdinç… - International Journal of …, 2022 - Elsevier
To ensure the autonomous power supply in microgrids (MGs) in stand-alone mode while
also maintaining stability, energy storage systems (ESSs) and demand-side flexibility can be …

Duck-curve mitigation in power grids with high penetration of PV generation

I Calero, CA Canizares… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Small-scale PV generation has become popular with residential customers in several
jurisdictions with high solar radiation, as an alternative to improve their carbon footprint and …

Deep reinforcement learning for demand response in distribution networks

S Bahrami, YC Chen, VWS Wong - IEEE Transactions on Smart …, 2020 - ieeexplore.ieee.org
Load aggregators can use demand response programs to motivate residential users toward
reducing electricity demand during peak time periods. This article proposes a demand …

Flexibility of grid interactive water heaters: The situation in the US

ML Di Silvestre, ER Sanseverino, E Telaretti… - … and Sustainable Energy …, 2023 - Elsevier
Among different solutions to increase flexibility of electricity system, grid interactive water
heaters (GIWHs) can make a large contribution, against a relatively low cost. They can …

Reinforcement learning-based pricing and incentive strategy for demand response in smart grids

EJ Salazar, M Jurado, ME Samper - Energies, 2023 - mdpi.com
International agreements support the modernization of electricity networks and renewable
energy resources (RES). However, these RES affect market prices due to resource …