Highly accurate energy consumption forecasting model based on parallel LSTM neural networks
出版年份 2021 全文链接
标题
Highly accurate energy consumption forecasting model based on parallel LSTM neural networks
作者
关键词
Long short term memory, Energy consumption, Time series data analysis, Forecasting, Singular spectrum analysis
出版物
ADVANCED ENGINEERING INFORMATICS
Volume 51, Issue -, Pages 101442
出版商
Elsevier BV
发表日期
2021-11-08
DOI
10.1016/j.aei.2021.101442
参考文献
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。- Forecasting national and regional level intensive care unit bed demand during COVID-19: The case of Italy
- (2021) Simone Gitto et al. PLoS One
- Coupling Singular Spectrum Analysis with Least Square Support Vector Machine to Improve Accuracy of SPI Drought Forecasting
- (2021) Quoc Bao Pham et al. WATER RESOURCES MANAGEMENT
- Chiller fault detection and diagnosis with anomaly detective generative adversarial network
- (2021) Ke Yan BUILDING AND ENVIRONMENT
- Occupancy-based energy consumption modelling using machine learning algorithms for institutional buildings
- (2021) Prashant Anand et al. ENERGY AND BUILDINGS
- Siamese Neural Network Based Few-Shot Learning for Anomaly Detection in Industrial Cyber-Physical Systems
- (2021) Xiaokang Zhou et al. IEEE Transactions on Industrial Informatics
- Forecasting Severe Weather with Random Forests
- (2020) Aaron J. Hill et al. MONTHLY WEATHER REVIEW
- Combination of cuckoo search and wavelet neural network for midterm building energy forecast
- (2020) Zhi Yuan et al. ENERGY
- Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm
- (2020) Gholamreza Memarzadeh et al. ELECTRIC POWER SYSTEMS RESEARCH
- Forecasting Transportation Network Speed Using Deep Capsule Networks With Nested LSTM Models
- (2020) Xiaolei Ma et al. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
- Daily natural gas consumption forecasting via the application of a novel hybrid model
- (2019) Nan Wei et al. APPLIED ENERGY
- Conventional models and artificial intelligence-based models for energy consumption forecasting: A review
- (2019) Nan Wei et al. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING
- Effective energy consumption forecasting using enhanced bagged echo state network
- (2019) Huanling Hu et al. ENERGY
- Integration of time series forecasting in a dynamic decision support system for multiple reservoir management to conserve water sources
- (2018) Hamed Zamani Sabzi et al. Energy Sources Part A-Recovery Utilization and Environmental Effects
- Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy
- (2018) Ke Yan et al. Energies
- Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction
- (2017) Xiaolei Ma et al. SENSORS
- MLP and MLR models for instantaneous thermal efficiency prediction of solar still under hyper-arid environment
- (2016) Ahmed F. Mashaly et al. COMPUTERS AND ELECTRONICS IN AGRICULTURE
- Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach
- (2015) Xiaoshu Lü et al. APPLIED ENERGY
- The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes
- (2015) Jack Kelly et al. Scientific Data
- Variational Mode Decomposition
- (2014) Konstantin Dragomiretskiy et al. IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Empirical Wavelet Transform
- (2013) Jerome Gilles IEEE TRANSACTIONS ON SIGNAL PROCESSING
- Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: A case study of China
- (2012) Yuanyuan Wang et al. ENERGY POLICY
- Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression
- (2010) Kadir Kavaklioglu APPLIED ENERGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started