A deep learning framework for building energy consumption forecast
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …
impact, upsurges the need for the design of reliable energy demand forecast models. This …
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
With the rapid growth in the volume of relevant and available data, feature engineering is
emerging as a popular research subject in data-driven building energy prediction owing to …
emerging as a popular research subject in data-driven building energy prediction owing to …
Robust framework based on hybrid deep learning approach for short term load forecasting of building electricity demand
Buildings consume about half of the global electrical energy, and an accurate prediction of
their electricity consumption is crucial for building microgrids' efficient and reliable …
their electricity consumption is crucial for building microgrids' efficient and reliable …
Robust short-term electrical load forecasting framework for commercial buildings using deep recurrent neural networks
This paper presents a robust short-term electrical load forecasting framework that can
capture variations in building operation, regardless of building type and location. Nine …
capture variations in building operation, regardless of building type and location. Nine …
A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction
I Karijadi, SY Chou - Energy and Buildings, 2022 - Elsevier
An accurate method for building energy consumption prediction is important for building
energy management systems. However, building energy consumption data often exhibits …
energy management systems. However, building energy consumption data often exhibits …
Interpretable building energy consumption forecasting using spectral clustering algorithm and temporal fusion transformers architecture
Accurate building energy consumption forecasting is crucial for developing efficient building
energy management systems, improving energy efficiency, and local building energy …
energy management systems, improving energy efficiency, and local building energy …
A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine
Residential electricity consumption accounts for a large proportion of the primary energy
consumption in China. Building energy management can effectively improve energy …
consumption in China. Building energy management can effectively improve energy …
[HTML][HTML] Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters
M Lumbreras, R Garay-Martinez, B Arregi… - Energy, 2022 - Elsevier
An accurate characterization and prediction of heat loads in buildings connected to a District
Heating (DH) network is crucial for the effective operation of these systems. The high …
Heating (DH) network is crucial for the effective operation of these systems. The high …
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
HSIC bottleneck based distributed deep learning model for load forecasting in smart grid with a comprehensive survey
Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …