[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 …
Review and prospect of data-driven techniques for load forecasting in integrated energy systems
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …
recognized lately as an effective approach to accommodate large-scale renewables and …
Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition
SR Moreno, LO Seman, SF Stefenon… - Energy, 2024 - Elsevier
Due to technological advancements, wind energy has emerged as a prominent renewable
power source. However, the intermittent nature of wind poses challenges in accurately …
power source. However, the intermittent nature of wind poses challenges in accurately …
Distributional neural networks for electricity price forecasting
We present a novel approach to probabilistic electricity price forecasting which utilizes
distributional neural networks. The model structure is based on a deep neural network …
distributional neural networks. The model structure is based on a deep neural network …
Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm
T Gao, D Niu, Z Ji, L Sun - Energy, 2022 - Elsevier
Mid-term electricity demand forecasting plays an important role in ensuring the operational
safety of the power system and the economic efficiency of grid companies. Most studies …
safety of the power system and the economic efficiency of grid companies. Most studies …
[HTML][HTML] Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model
D Li, Y Tan, Y Zhang, S Miao, S He - … Journal of Electrical Power & Energy …, 2023 - Elsevier
The growth of distributed renewable energy and demand-side responsiveness has
increased the difficulty of mid-term hourly load time-series forecasting. This study presents a …
increased the difficulty of mid-term hourly load time-series forecasting. This study presents a …
[HTML][HTML] ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands
This study introduces an energy management method that smooths electricity consumption
and shaves peaks by scheduling the operating hours of water pumping stations in a smart …
and shaves peaks by scheduling the operating hours of water pumping stations in a smart …
CO2 emission reduction effect of photovoltaic industry through 2060 in China
X Guo, Y Dong, D Ren - Energy, 2023 - Elsevier
In the coming four-decade, China will face serious challenge while shifting to carbon neutral.
Photovoltaic (PV) power, as one of the most promising clean energies, is seen as an …
Photovoltaic (PV) power, as one of the most promising clean energies, is seen as an …
[HTML][HTML] Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks
One of the most controversial issues in the mid-term load forecasting literature is the
treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a …
treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a …
Very short-term residential load forecasting based on deep-autoformer
Very short-term load forecasting (VSLTF) plays an essential role in guaranteeing effective
electricity dispatching and generating in residential microgrid systems. However, the …
electricity dispatching and generating in residential microgrid systems. However, the …