Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives
Energy flexibility, through short-term demand-side management (DSM) and energy storage
technologies, is now seen as a major key to balancing the fluctuating supply in different …
technologies, is now seen as a major key to balancing the fluctuating supply in different …
Scenario-based multi-objective optimization strategy for rural PV-battery systems
Y Zhi, X Yang - Applied Energy, 2023 - Elsevier
Increasing the proportion of photovoltaic (PV) electricity in power systems is effective for
achieving carbon neutrality. However, PV electricity is unstable and random, and direct …
achieving carbon neutrality. However, PV electricity is unstable and random, and direct …
[HTML][HTML] Risk-averse and flexi-intelligent scheduling of microgrids based on hybrid Boltzmann machines and cascade neural network forecasting
The future of energy flexibility in microgrids (MGs) is steering towards a highly granular
control of the end-user customers. This calls for more highly accurate uncertainty forecasting …
control of the end-user customers. This calls for more highly accurate uncertainty forecasting …
A simplified assessment method based on Hooke's law to estimate the grid-friendly ability of buildings
L Yue, J Niu, Z Tian, Q Lin, Y Lu - Renewable Energy, 2024 - Elsevier
The development of grid-friendly buildings helps to enhance the supply–demand interaction,
thereby improving the energy system efficiency. The relevant evaluation indicators are …
thereby improving the energy system efficiency. The relevant evaluation indicators are …
Transfer learning-based adaptive recursive neural network for short-term non-stationary building heating load prediction
Y Zhou, X Li, Y Liu, R Wei - Journal of Building Engineering, 2023 - Elsevier
Building energy consumption is a non-stationary time series, and its distribution law
changes over time. Traditional machine-learning models are prone to model shift, which …
changes over time. Traditional machine-learning models are prone to model shift, which …
A novel method of creating machine learning-based time series meta-models for building energy analysis
G Li, W Tian, H Zhang, X Fu - Energy and Buildings, 2023 - Elsevier
The meta-models have been widely used to replace computationally expensive engineering-
based building energy models for model calibration, sensitivity analysis, and performance …
based building energy models for model calibration, sensitivity analysis, and performance …
Leveraging the machine learning techniques for demand-side flexibility–A comprehensive review
The increasing reliance on renewable energy sources poses challenges in managing the
grid, necessitating a focus on energy efficiency and grid stability for a smooth energy …
grid, necessitating a focus on energy efficiency and grid stability for a smooth energy …
Day-ahead demand response potential forecasting model considering dynamic spatial-temporal correlation based on directed graph structure
The day-ahead demand response (DR) potential forecasting can provide reference
information for load aggregators (LAs) to participate in bidding offers in the electricity market …
information for load aggregators (LAs) to participate in bidding offers in the electricity market …
Effect of sample interval on the parameter identification results of RC equivalent circuit models of li-ion battery: An investigation based on HPPC test data
H Zhang, C Deng, Y Zong, Q Zuo, H Guo, S Song… - Batteries, 2022 - mdpi.com
The validity of the equivalent circuit model (ECM), which is crucial for the development of
lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of …
lithium-ion batteries (LIBs) and state evaluation, is primarily dependent on the precision of …
A novel short-term multi-energy load forecasting method for integrated energy system based on two-layer joint modal decomposition and dynamic optimal ensemble …
Z Lin, T Lin, J Li, C Li - Applied Energy, 2025 - Elsevier
Accurate short-term multi-energy load forecasting is the cornerstone for optimal dispatch and
stable operation of integrated energy system (IES). However, due to the complexity and …
stable operation of integrated energy system (IES). However, due to the complexity and …