AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
components and functionalities required for analyzing and operating buildings. However, in …
Building energy prediction using artificial neural networks: A literature survey
C Lu, S Li, Z Lu - Energy and Buildings, 2022 - Elsevier
Building Energy prediction has emerged as an active research area due to its potential in
improving energy efficiency in building energy management systems. Essentially, building …
improving energy efficiency in building energy management systems. Essentially, building …
Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features
Z Zhao, S Yun, L Jia, J Guo, Y Meng, N He, X Li… - … Applications of Artificial …, 2023 - Elsevier
Accurate and reliable short-term forecasting of wind power is vital for balancing energy and
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …
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 …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
BiCuDNNLSTM-1dCNN—A hybrid deep learning-based predictive model for stock price prediction
Within last decade, the investing habits of people is rapidly increasing towards stock market.
The nonlinearity and high volatility of stock prices have made it challenging to predict stock …
The nonlinearity and high volatility of stock prices have made it challenging to predict stock …
Spatial-temporal residential short-term load forecasting via graph neural networks
Electric load forecasting, especially short-term load forecasting, is of significant importance
for the safe and efficient operation of power grids. With the wide adoption of advanced smart …
for the safe and efficient operation of power grids. With the wide adoption of advanced smart …
Inception inspired CNN-GRU hybrid network for human activity recognition
Abstract Human Activity Recognition (HAR) involves the recognition of human activities
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
using sensor data. Most of the techniques for HAR involve hand-crafted features and hence …
Multi-dimensional prediction method based on Bi-LSTMC for ship roll
Y Wang, H Wang, B Zhou, H Fu - Ocean Engineering, 2021 - Elsevier
When ships sail in the sea, they will move irregularly due to the influence of strong wind,
waves and other complex marine environment. Among them, the ship roll is very important to …
waves and other complex marine environment. Among them, the ship roll is very important to …