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Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities
Related publications (82)
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Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...
EPFL2024Beyond the average consumer: Mapping the potential of demand-side management among patterns of appliance usage
Claudia Rebeca Binder Signer, Selin Yilmaz, Matteo Barsanti
To support the decarbonisation of the power sector and offset the volatility of a system with high levels of renewables, there is growing interest in residential Demand-Side Management (DSM) solutions. Traditional DSM strategies require consumers to active ...
2024Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning
Berend Smit, Xiaoqi Zhang, Sauradeep Majumdar, Hyunsoo Park
The combination of several interesting characteristics makes metal-organic frameworks (MOFs) a highly sought-after class of nanomaterials for a broad range of applications like gas storage and separation, catalysis, drug delivery, and so on. However, the e ...
Royal Soc Chemistry2024DIET Controller: Dynamic Indoor Environment using Deep Reinforcement Learning
Heating, Ventilation, and Air Conditioning (HVAC) Systems utilize much energy, accounting for 40% of total building energy use. The temperatures in buildings are commonly held within narrow limits, leading to higher energy use. Measurements from office bui ...
EPFL2023PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling
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Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for ...
Basel2023Energy, SBS symptoms, and productivity in Swiss open-space offices: Economic evaluation of standard, actual, and optimum scenarios
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The fundamental aspiration of new-generation high-performing buildings is to reduce energy use while securing indoor environmental quality conducive to human health and productivity. However, existing frameworks for identifying Key Performance Indicators ( ...
PERGAMON-ELSEVIER SCIENCE LTD2023From domestic energy demand to household activity patterns
Michel Bierlaire, Timothy Michael Hillel, Negar Rezvany
Residential building energy usage can be considered as being derived from the activity patterns of individuals inside the home. As such an activity-based energy demand model that can create in-home energy usage profiles from household activity patterns is ...
2023Hardware-Software co-design Methodologies for Edge AI Optimization
ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...
EPFL2023RECA: A Multi-Task Deep Reinforcement Learning-Based Recommender System for Co-Optimizing Energy, Comfort and Air Quality in Commercial Buildings
We present the design and implementation of RECA, a novel human-centric recommender system for co-optimizing energy consumption, comfort and air quality in commercial buildings. Existing works generally optimize these objectives separately, or by only cont ...
Association for Computing Machinery2023Safe multi-agent deep reinforcement learning for joint bidding and maintenance scheduling of generation units
This paper proposes a safe reinforcement learning algorithm for generation bidding decisions and unit maintenance scheduling in a competitive electricity market environment. In this problem, each unit aims to find a bidding strategy that maximizes its reve ...
ELSEVIER SCI LTD2023