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[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
[HTML][HTML] Physical energy and data-driven models in building energy prediction: A review
The difficulty in balancing energy supply and demand is increasing due to the growth of
diversified and flexible building energy resources, particularly the rapid development of …
diversified and flexible building energy resources, particularly the rapid development of …
[HTML][HTML] Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing
The increasing integration of variable renewable technologies at distribution feeders, mainly
solar photovoltaic (PV) systems, presents new challenges to grid operators for accurately …
solar photovoltaic (PV) systems, presents new challenges to grid operators for accurately …
Interpretable feature selection and deep learning for short-term probabilistic PV power forecasting in buildings using local monitoring data
Accurate probabilistic forecasting of photovoltaic (PV) power is crucial for optimizing energy
scheduling in smart buildings and ensuring the low-carbon, efficient operation of building …
scheduling in smart buildings and ensuring the low-carbon, efficient operation of building …
A dynamic intelligent building retrofit decision-making model in response to climate change
Building energy-saving retrofitting has become an essential way for the building sector to
cope with climate change. Furthermore, climate change affects building retrofit strategies …
cope with climate change. Furthermore, climate change affects building retrofit strategies …
An adaptive federated learning system for community building energy load forecasting and anomaly prediction
Energy load forecasting is critical for sustainable building development and management.
Although the energy data could be collected through Internet of Things (IoT) systems, it is a …
Although the energy data could be collected through Internet of Things (IoT) systems, it is a …
Similarity learning-based fault detection and diagnosis in building HVAC systems with limited labeled data
Abstract Machine learning has been widely adopted for fault detection and diagnosis (FDD)
in heating, ventilation and air conditioning (HVAC) systems over the past decade due to the …
in heating, ventilation and air conditioning (HVAC) systems over the past decade due to the …
A novel seasonal segmentation approach for day-ahead load forecasting
A Sharma, SK Jain - Energy, 2022 - Elsevier
Day-ahead load forecasting plays a crucial role in operation and management of power
systems. Weather conditions have a significant impact on daily load profile, hence, it follows …
systems. Weather conditions have a significant impact on daily load profile, hence, it follows …
Exploring automated energy optimization with unstructured building data: A multi-agent based framework leveraging large language models
The building sector is a significant energy consumer, making building energy optimization
crucial for reducing energy demand. Automating energy optimization tasks eases the …
crucial for reducing energy demand. Automating energy optimization tasks eases the …
An effective dimensionality reduction approach for short-term load forecasting
Y Yang, Z Wang, Y Gao, J Wu, S Zhao… - Electric Power Systems …, 2022 - Elsevier
Accurate power load forecasting has a significant effect on a smart grid by ensuring effective
supply and dispatching of power. However, electric load data generally possesses the …
supply and dispatching of power. However, electric load data generally possesses the …