4.7 Article

Interpretable deep learning model for building energy consumption prediction based on attention mechanism

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Environmental Sciences

A Spatial-Temporal Interpretable Deep Learning Model for improving interpretability and predictive accuracy of satellite-based PM2.5

Xing Yan et al.

Summary: The development of a new Spatial-Temporal Interpretable Deep Learning Model (SIDLM) has been detailed in this study to enhance the interpretability and predictive accuracy of satellite-based PM2.5 measurements. The SIDLM demonstrated higher accuracy than five machine learning inversion methods and showed strong influence of certain districts on PM2.5 concentrations in urban areas such as Beijing. Overall, the new model has promising applications in deep learning-based predictions and spatiotemporal analysis of other earth observation data.

ENVIRONMENTAL POLLUTION (2021)

Article Computer Science, Artificial Intelligence

An interpretable deep-learning model for early prediction of sepsis in the emergency department

Dongdong Zhang et al.

Summary: The study introduced a LSTM-based model to predict the onset of sepsis, achieving high prediction accuracy. The model utilized event embedding, time encoding, attention mechanism, and global max pooling techniques to improve performance and interpret results effectively.

PATTERNS (2021)

Article Engineering, Electrical & Electronic

Load demand forecasting of residential buildings using a deep learning model

Lulu Wen et al.

ELECTRIC POWER SYSTEMS RESEARCH (2020)

Article Construction & Building Technology

Sequence-to-sequence neural networks for short-term electrical load forecasting in commercial office buildings

Elliott Skomski et al.

ENERGY AND BUILDINGS (2020)

Article Computer Science, Artificial Intelligence

Interpretable spatio-temporal attention LSTM model for flood forecasting

Yukai Ding et al.

NEUROCOMPUTING (2020)

Article Green & Sustainable Science & Technology

Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks

Bixuan Gao et al.

RENEWABLE ENERGY (2020)

Article Energy & Fuels

How to model European electricity load profiles using artificial neural networks

Christian Behm et al.

APPLIED ENERGY (2020)

Article Construction & Building Technology

Recurrent inception convolution neural network for multi short-term load forecasting

Junhong Kim et al.

ENERGY AND BUILDINGS (2019)

Article Construction & Building Technology

Random Forest based hourly building energy prediction

Zeyu Wang et al.

ENERGY AND BUILDINGS (2018)

Article Construction & Building Technology

Transfer learning with seasonal and trend adjustment for cross-building energy forecasting

Mauro Ribeiro et al.

ENERGY AND BUILDINGS (2018)

Article Construction & Building Technology

A novel ensemble learning approach to support building energy use prediction

Zeyu Wang et al.

ENERGY AND BUILDINGS (2018)

Article Computer Science, Artificial Intelligence

Neural network based optimization approach for energy demand prediction in smart grid

K. Muralitharan et al.

NEUROCOMPUTING (2018)

Review Green & Sustainable Science & Technology

A review of data-driven building energy consumption prediction studies

Kadir Amasyali et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)

Article Construction & Building Technology

Research on short-term and ultra-short-term cooling load prediction models for office buildings

Yan Ding et al.

ENERGY AND BUILDINGS (2017)

Article Computer Science, Hardware & Architecture

ImageNet Classification with Deep Convolutional Neural Networks

Alex Krizhevsky et al.

COMMUNICATIONS OF THE ACM (2017)

Proceedings Paper Automation & Control Systems

Simultaneous Estimation of Contraction Ratio and Parameter of McKibben Pneumatic Artificial Muscle Model Using Log-Normalized Unscented Kalman Filter

Takashi Kodama et al.

PROCEEDINGS OF 2016 IEEE 4TH INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS, NETWORKS, AND APPLICATIONS (CPSNA) (2016)

Article Construction & Building Technology

Short-term electricity load forecasting of buildings in microgrids

Hamed Chitsaz et al.

ENERGY AND BUILDINGS (2015)

Article Construction & Building Technology

China's energy consumption in the building sector: A life cycle approach

Yan Zhang et al.

ENERGY AND BUILDINGS (2015)

Review Green & Sustainable Science & Technology

Regression analysis for prediction of residential energy consumption

Nelson Fumo et al.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)

Article Engineering, Electrical & Electronic

Deep Neural Networks for Acoustic Modeling in Speech Recognition

Geoffrey Hinton et al.

IEEE SIGNAL PROCESSING MAGAZINE (2012)