A new interval prediction methodology for short-term electric load forecasting based on pattern recognition
Published 2021 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
A new interval prediction methodology for short-term electric load forecasting based on pattern recognition
Authors
Keywords
Electricity demand, Pattern recognition, Prediction intervals, Short-term forecasting
Journal
APPLIED ENERGY
Volume 297, Issue -, Pages 117173
Publisher
Elsevier BV
Online
2021-06-01
DOI
10.1016/j.apenergy.2021.117173
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A novel composite electricity demand forecasting framework by data processing and optimized support vector machine
- (2020) Ping Jiang et al. APPLIED ENERGY
- A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles
- (2020) Xavier Serrano-Guerrero et al. Energies
- Assessing and Comparing Short Term Load Forecasting Performance
- (2020) Pekka Koponen et al. Energies
- Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
- (2018) Aowabin Rahman et al. APPLIED ENERGY
- Residential probabilistic load forecasting: A method using Gaussian process designed for electric load data
- (2018) Mahmoud Shepero et al. APPLIED ENERGY
- Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes
- (2018) D.W. van der Meer et al. APPLIED ENERGY
- Estimates of energy consumption in China using a self-adaptive multi-verse optimizer-based support vector machine with rolling cross-validation
- (2018) Xiaoyu Wang et al. ENERGY
- Short-term power load probability density forecasting based on Yeo-Johnson transformation quantile regression and Gaussian kernel function
- (2018) Yaoyao He et al. ENERGY
- Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination
- (2018) Wenjie Zhang et al. ENERGY
- Statistical methodology to assess changes in the electrical consumption profile of buildings
- (2018) Xavier Serrano-Guerrero et al. ENERGY AND BUILDINGS
- Probabilistic forecasting of solar power, electricity consumption and net load: Investigating the effect of seasons, aggregation and penetration on prediction intervals
- (2018) D.W. van der Meer et al. SOLAR ENERGY
- Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption
- (2017) Muhammad Waseem Ahmad et al. ENERGY AND BUILDINGS
- Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network
- (2017) Weicong Kong et al. IEEE Transactions on Smart Grid
- Deep Neural Network Based Demand Side Short Term Load Forecasting
- (2016) Seunghyoung Ryu et al. Energies
- Upgrade of an artificial neural network prediction method for electrical consumption forecasting using an hourly temperature curve model
- (2013) Carlos Roldán-Blay et al. ENERGY AND BUILDINGS
- State of the art in building modelling and energy performances prediction: A review
- (2013) Aurélie Foucquier et al. RENEWABLE & SUSTAINABLE ENERGY REVIEWS
- New artificial neural network prediction method for electrical consumption forecasting based on building end-uses
- (2011) Guillermo Escrivá-Escrivá et al. ENERGY AND BUILDINGS
- Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances
- (2011) A. Khosravi et al. IEEE TRANSACTIONS ON NEURAL NETWORKS
- Construction of Optimal Prediction Intervals for Load Forecasting Problems
- (2010) Abbas Khosravi et al. IEEE TRANSACTIONS ON POWER SYSTEMS
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started