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。Short-term individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods
Ali Forootani et al.
ELECTRIC POWER SYSTEMS RESEARCH (2022)
Introducing explainability in sequence-to-sequence learning for short-term load forecasting
Gonca Guerses-Tran et al.
ELECTRIC POWER SYSTEMS RESEARCH (2022)
Temperature and Residential Electricity Demand for Heating and Cooling in G7 Economies: A Method of Moments Panel Quantile Regression Approach
Chukwuemeka Chinonso Emenekwe et al.
CLIMATE (2022)
Modeling Energy Demand-A Systematic Literature Review
Paul Anton Verwiebe et al.
ENERGIES (2021)
A novel asynchronous deep reinforcement learning model with adaptive early forecasting method and reward incentive mechanism for short-term load forecasting
Wenyu Zhang et al.
ENERGY (2021)
Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning
Yi Hu et al.
APPLIED ENERGY (2021)
Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling
Guo-Feng Fan et al.
UTILITIES POLICY (2021)
Short-term apartment-level load forecasting using a modified neural network with selected auto-regressive features
Lechen Li et al.
APPLIED ENERGY (2021)
Aggregated short-term load forecasting for heterogeneous buildings using machine learning with peak estimation
Amine Bellahsen et al.
ENERGY AND BUILDINGS (2021)
Short-term electric load forecasting based on improved Extreme Learning Machine Mode
Jie Yuan et al.
ENERGY REPORTS (2021)
A bottom-up short-term residential load forecasting approach based on appliance characteristic analysis and multi-task learning
Shouxiang Wang et al.
ELECTRIC POWER SYSTEMS RESEARCH (2021)
Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France
David Obst et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2021)
Generalizability Improvement of Deep Learning-Based Non-Intrusive Load Monitoring System Using Data Augmentation
Hasan Rafiq et al.
IEEE TRANSACTIONS ON SMART GRID (2021)
Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting
A. S. Khwaja et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
Internet of Things (IoT) and the Energy Sector
Naser Hossein Motlagh et al.
ENERGIES (2020)
A Study of Local Climate Zones in Abu Dhabi with Urban Weather Stations and Numerical Simulations
Prajowal Manandhar et al.
SUSTAINABILITY (2020)
Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine
Waqas Ahmad et al.
ENERGIES (2020)
Intra-day Electricity Demand and Temperature
Lames McCulloch et al.
ENERGY JOURNAL (2020)
Assessment of stacked unidirectional and bidirectional long short-term memory networks for electricity load forecasting
Sara Atef et al.
ELECTRIC POWER SYSTEMS RESEARCH (2020)
An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model
Dengyong Zhang et al.
ENERGIES (2020)
Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits
Meng Shen et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2020)
How to model European electricity load profiles using artificial neural networks
Christian Behm et al.
APPLIED ENERGY (2020)
The impact of different COVID-19 containment measures on electricity consumption in Europe
Alireza Bahmanyar et al.
ENERGY RESEARCH & SOCIAL SCIENCE (2020)
Overfitting remedy by sparsifying regularization on fully-connected layers of CNNs
Qi Xu et al.
NEUROCOMPUTING (2019)
Deep learning framework to forecast electricity demand
Jatin Bedi et al.
APPLIED ENERGY (2019)
Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation
Shree Krishna Acharya et al.
ENERGIES (2019)
Analysis of energy consumption profiles in residential buildings and impact assessment of a serious game on occupants' behavior
Tamas Csoknyai et al.
ENERGY AND BUILDINGS (2019)
A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction
Bin Li et al.
ENERGIES (2019)
Utility solar prices will continue to drop all over the world even without subsidies
Harry Apostoleris et al.
NATURE ENERGY (2019)
Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting
Peter Laurinec et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2019)
Climate change and residential electricity consumption in the Yangtze River Delta, China
Yating Li et al.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)
A comparison of six metamodeling techniques applied to building performance simulations
Torben Ostergard et al.
APPLIED ENERGY (2018)
A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
Jingjing Song et al.
APPLIED ENERGY (2018)
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches
Salah Bouktif et al.
ENERGIES (2018)
Performance Analysis of Short-Term Electricity Demand with Atmospheric Variables
Kamal Chapagain et al.
ENERGIES (2018)
Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm
Xing Zhang
ENERGIES (2018)
Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data
Magnus Dahl et al.
ENERGIES (2018)
A novel data-driven approach for residential electricity consumption prediction based on ensemble learning
Kunlong Chen et al.
ENERGY (2018)
Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm
Lin Wang et al.
ENERGY (2018)
Intelligent techniques for forecasting electricity consumption of buildings
K. P. Amber et al.
ENERGY (2018)
Forecasting methods in energy planning models
Kumar Biswajit Debnath et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)
A review of data-driven building energy consumption prediction studies
Kadir Amasyali et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2018)
Empirical Mode Decomposition Based Deep Learning for Electricity Demand Forecasting
Jatin Bedi et al.
IEEE ACCESS (2018)
Clustering-based forecasting method for individual consumers electricity load using time series representations
Peter Laurinec et al.
OPEN COMPUTER SCIENCE (2018)
Long-Short-Term Memory Network Based Hybrid Model for Short-Term Electrical Load Forecasting
Liwen Xu et al.
INFORMATION (2018)
Mean shift densification of scarce data sets in short-term electric power load forecasting for special days
Liviane Rego et al.
ELECTRICAL ENGINEERING (2017)
Modeling hourly consumption of electricity and district heat in non-residential buildings
A. Kipping et al.
ENERGY (2017)
Climatic influence on electricity consumption: The case of Singapore and Hong Kong
B. W. Ang et al.
ENERGY (2017)
Inverse modeling of the urban energy system using hourly electricity demand and weather measurements, Part 1: Black-box model
Afshin Afshari et al.
ENERGY AND BUILDINGS (2017)
Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation
Longquan Diao et al.
ENERGY AND BUILDINGS (2017)
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models
Zeyu Wang et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2017)
Electrical load forecasting models: A critical systematic review
Corentin Kuster et al.
SUSTAINABLE CITIES AND SOCIETY (2017)
A Review of Deep Learning Methods Applied on Load Forecasting
Abdulaziz Almalaq et al.
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA) (2017)
Forecasting the Daily Electricity Consumption in the Moscow Region Using Artificial Neural Networks
V. V. Ivanov et al.
PHYSICS OF PARTICLES AND NUCLEI LETTERS (2017)
A hybrid system for forecasting 24-h power load profile for Polish electric grid
Stanislaw Brodowski et al.
APPLIED SOFT COMPUTING (2017)
Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm
Xiaobo Zhang et al.
ELECTRIC POWER SYSTEMS RESEARCH (2017)
Framework to investigate energy conservation motivation and actions of building occupants: The case of a green campus in Abu Dhabi, UAE
Elie Azar et al.
APPLIED ENERGY (2017)
A study on estimation of aggregated electricity demand for one-hour-ahead forecast
Kei Morita et al.
APPLIED THERMAL ENGINEERING (2017)
Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption
Che-Jung Chang et al.
ADVANCED ENGINEERING INFORMATICS (2016)
Applied machine learning: Forecasting heat load in district heating system
Samuel Idowu et al.
ENERGY AND BUILDINGS (2016)
Short Term Electrical Load Forecasting Using Mutual Information Based Feature Selection with Generalized Minimum-Redundancy and Maximum-Relevance Criteria
Nantian Huang et al.
ENTROPY (2016)
Analysis of Dropout Learning Regarded as Ensemble Learning
Kazuyuki Hara et al.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT II (2016)
Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
Yuancheng Li et al.
ALGORITHMS (2016)
Probabilistic electric load forecasting: A tutorial review
Tao Hong et al.
INTERNATIONAL JOURNAL OF FORECASTING (2016)
Temperature effects on firms' electricity demand: An analysis of sectorial differences in Spain
Julian Moral-Carcedo et al.
APPLIED ENERGY (2015)
Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?
Nan Li et al.
APPLIED ENERGY (2015)
A hybrid self-adaptive Particle Swarm Optimization-Genetic Algorithm-Radial Basis Function model for annual electricity demand prediction
Shiwei Yu et al.
ENERGY CONVERSION AND MANAGEMENT (2015)
A Novel RBF Training Algorithm for Short-Term Electric Load Forecasting and Comparative Studies
Carlo Cecati et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines
Fazil Kaytez et al.
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS (2015)
Regression analysis for prediction of residential energy consumption
Nelson Fumo et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2015)
Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy
Rishee K. Jain et al.
APPLIED ENERGY (2014)
Dynamic factors in periodic time-varying regressions with an application to hourly electricity load modelling
Virginie Dordonnat et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2012)
Predicting future hourly residential electrical consumption: A machine learning case study
Richard E. Edwards et al.
ENERGY AND BUILDINGS (2012)
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition
Souhaib Ben Taieb et al.
EXPERT SYSTEMS WITH APPLICATIONS (2012)
A review on the prediction of building energy consumption
Hai-xiang Zhao et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2012)
Artificial neural networks for energy analysis of office buildings with daylighting
S. L. Wong et al.
APPLIED ENERGY (2010)
A decision tree method for building energy demand modeling
Zhun Yu et al.
ENERGY AND BUILDINGS (2010)
Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings
Joshua Kneifel
ENERGY AND BUILDINGS (2010)
Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series
Xiaozhe Wang et al.
NEUROCOMPUTING (2009)
The impacts of climatic and economic factors on residential electricity consumption of Bangkok Metropolis
Kiattiporn Wangpattarapong et al.
ENERGY AND BUILDINGS (2008)
Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks
Geoffrey K. F. Tso et al.
ENERGY (2007)
Electric energy restructuring in the European Union:: Integration, subsidiarity and the challenge of harmonization
Roberto J. Serralles
ENERGY POLICY (2006)
Modeling and predicting building's energy use with artificial neural networks: Methods and results
S. Karatasou et al.
ENERGY AND BUILDINGS (2006)
A comparison of univariate methods for forecasting electricity demand up to a day ahead
JW Taylor et al.
INTERNATIONAL JOURNAL OF FORECASTING (2006)
Large neural networks for electricity load forecasting: Are they overfitted?
HS Hippert et al.
INTERNATIONAL JOURNAL OF FORECASTING (2005)
Prediction of hourly energy consumption in buildings based on a feedback artificial neural network
PA González et al.
ENERGY AND BUILDINGS (2005)
Neural networks for short-term load forecasting: A review and evaluation
HS Hippert et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2001)