Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives
Smart buildings play a crucial role toward decarbonizing society, as globally buildings emit
about one-third of greenhouse gases. In the last few years, machine learning has achieved …
about one-third of greenhouse gases. In the last few years, machine learning has achieved …
[HTML][HTML] Next-generation energy systems for sustainable smart cities: Roles of transfer learning
Smart cities attempt to reach net-zero emissions goals by reducing wasted energy while
improving grid stability and meeting service demand. This is possible by adopting next …
improving grid stability and meeting service demand. This is possible by adopting next …
[HTML][HTML] Transfer learning in demand response: A review of algorithms for data-efficient modelling and control
A number of decarbonization scenarios for the energy sector are built on simultaneous
electrification of energy demand, and decarbonization of electricity generation through …
electrification of energy demand, and decarbonization of electricity generation through …
Physics-informed machine learning for modeling and control of dynamical systems
Physics-informed machine learning (PIML) is a set of methods and tools that systematically
integrate machine learning (ML) algorithms with physical constraints and abstract …
integrate machine learning (ML) algorithms with physical constraints and abstract …
Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics
In recent years deep neural networks have been proposed as a lightweight data-driven
model to capture high-dimensional, nonlinear physical processes to predict building thermal …
model to capture high-dimensional, nonlinear physical processes to predict building thermal …
Learn to chill: Intelligent chiller scheduling using meta-learning and deep reinforcement learning
P Manoharan, MP Venkat, S Nagarathinam… - Proceedings of the 8th …, 2021 - dl.acm.org
Centralized chiller plants with multiple chillers are typically over-provisioned. Therefore,
intelligent scheduling is required for the supply (operating chillers) to efficiently meet the …
intelligent scheduling is required for the supply (operating chillers) to efficiently meet the …
A Lifelong Meta-Learning Approach for Learning Deep Grey-box Representative Thermal Dynamics Models for Residential Buildings
Thermal dynamics models of residential buildings are crucial for managing energy use and
maintaining desirable indoor environment quality, in the context of decarbonizing the electric …
maintaining desirable indoor environment quality, in the context of decarbonizing the electric …
Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series
Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However,
the non-linearity, non-stationarity, and randomness characterizing electricity demand time …
the non-linearity, non-stationarity, and randomness characterizing electricity demand time …
A Rational Plan of Energy Performance Contracting in an Educational Building: A Case Study
ZH Mohamad Munir, N Ahmad Ludin, MM Junedi… - Sustainability, 2023 - mdpi.com
Energy performance contracting (EPC) is the best solution for an educational building to
implement energy conservation measures (ECMs) because of its high capital expenditure …
implement energy conservation measures (ECMs) because of its high capital expenditure …
A Transfer Learning Approach to Create Energy Forecasting Models for Building Fleets
The development of accurate energy prediction models plays a significant role in achieving
sustainability in smart cities. However, stakeholders such as municipalities face the problem …
sustainability in smart cities. However, stakeholders such as municipalities face the problem …