Research graph
References from Data analytics in the electricity sector – A quantitative and qualitative literature review. Local targets link to admitted publications; unresolved targets remain external evidence.
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Power utility nontechnical loss analysis with extreme learning machine method
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10.1109/tpwrs.2002.1007902 · 2002 · External reference
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10.1016/j.rser.2017.01.053 · 2017 · External reference
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Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques
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Robust decentralized neural networks based LFC in a deregulated power system
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10.1109/tpwrs.2007.907583 · 2007 · External reference
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Online monitoring of voltage stability margin using an artificial neural network
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External reference
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History and trends in solar irradiance and PV power forecasting: a preliminary assessment and review using text mining
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Evaluation of neural network based real time maximum power tracking controller for PV system
10.1109/60.464880 · 1995 · External reference
A load predictive energy management system for supercapacitor-battery hybrid energy storage system in solar application using the support vector machine
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Analysis of different combinations of meteorological parameters in predicting the horizontal global solar radiation with ann approach: a case study
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AWNN-Assisted Wind power forecasting using feed-Forward neural network
10.1109/tste.2011.2182215 · 2012 · External reference
Forecasting energy demand, wind generation and carbon dioxide emissions in ireland using evolutionary neural networks
10.1016/j.energy.2018.04.192 · 2018 · External reference
Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in queensland
10.1016/j.rser.2017.01.114 · 2017 · External reference
Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting
10.1016/j.enconman.2018.11.074 · 2019 · External reference
Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation
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Solar radiation prediction using different techniques: model evaluation and comparison
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Online 24-h solar power forecasting based on weather type classification using artificial neural network
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Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning
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Short–mid-term solar power prediction by using artificial neural networks
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Development of a neural network model to predict daily solar radiation
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Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: saudi arabia case study
10.1016/j.enconman.2015.07.083 · 2015 · External reference
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Solar radiation prediction using artificial neural network techniques: a review
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Hourly solar radiation forecasting using optimal coefficient 2-d linear filters and feed-forward neural networks
10.1016/j.solener.2008.02.003 · 2008 · External reference
Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: a case study in china
10.1016/j.enconman.2018.02.087 · 2018 · External reference
A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset
10.1016/j.apenergy.2016.01.130 · 2016 · External reference
Short-term photovoltaic solar power forecasting using a hybrid wavelet-pso-svm model based on scada and meteorological information
10.1016/j.renene.2017.11.011 · 2018 · External reference
Short-term wind power ensemble prediction based on gaussian processes and neural networks
10.1109/tsg.2013.2280649 · 2014 · External reference
Short-term wind power prediction based on lssvm–gsa model
10.1016/j.enconman.2015.05.065 · 2015 · External reference
Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods
10.1016/j.enconman.2016.01.023 · 2016 · External reference
Evaluation of hybrid forecasting approaches for wind speed and power generation time series
10.1016/j.rser.2012.02.044 · 2012 · External reference
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and bp neural network optimized by firefly algorithm
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Probabilistic forecasting of wind power generation using extreme learning machine
10.1109/tpwrs.2013.2287871 · 2014 · External reference
Short-term electric power forecasting using dual-stage hierarchical wavelet-Particle swarm optimization-Adaptive neuro-Fuzzy inference system PSO-ANFIS approach based on climate change
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Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models
10.1016/j.ijforecast.2008.08.007 · 2008 · External reference
Short-term wind power prediction based on improved chicken algorithm optimization support vector machine
10.3390/su11020512 · 2019 · External reference
A better measure of relative prediction accuracy for model selection and model estimation
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Deep learning based ensemble approach for probabilistic wind power forecasting
10.1016/j.apenergy.2016.11.111 · 2017 · External reference
A comparison between the ECMWF and COSMO ensemble prediction systems applied to short-term wind power forecasting on real data
10.1016/j.apenergy.2013.02.041 · 2013 · External reference
Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and markov model
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Unresolved reference
External reference
A novel approach to forecast electricity price for PJM using neural network and similar days method
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An adaptive wavelet neural network-based energy price forecasting in electricity markets
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Day-ahead price forecasting in restructured power systems using artificial neural networks
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Day-ahead deregulated electricity market price forecasting using recurrent neural network
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Applying ARMA–GARCH approaches to forecasting short-term electricity prices
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A bat optimized neural network and wavelet transform approach for short-term price forecasting
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Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets
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Neural networks approach to forecast several hour ahead electricity prices and loads in deregulated market
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Forecasting next-day price of electricity in the spanish energy market using artificial neural networks
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Day-ahead electricity price forecasting by modified relief algorithm and hybrid neural network
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A novel model: dynamic choice artificial neural network (DCANN) for an electricity price forecasting system
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ARIMA Models to predict next-day electricity prices
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A garch forecasting model to predict day-ahead electricity prices
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Dynamic hybrid model for short-term electricity price forecasting
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Electricity market price spike forecast with data mining techniques
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Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme
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Mid-term electricity market clearing price forecasting using multiple least squares support vector machines
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Understanding the fine structure of electricity prices
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Another look at measures of forecast accuracy
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Neural-network-based load modeling and its use in voltage stability analysis
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Neural network-based model design for short-term load forecast in distribution systems
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Voltage stability prediction using active machine learning
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Reduction of frequency fluctuation for wind farm connected power systems by an adaptive artificial neural network controlled energy capacitor system
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Load frequency control of a dynamic interconnected power system using generalised hopfield neural network based self-adaptive pid controller
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Recurrent fuzzy neural network by using feedback error learning approaches for lfc in interconnected power system
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Data-driven prediction of load curtailment in incentive-based demand response system
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Prediction of transmission line overloading using intelligent technique
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Electricity demand forecasting over italy: potential benefits using numerical weather prediction models
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ARIMA-Based decoupled time series forecasting of electric vehicle charging demand for stochastic power system operation
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A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon
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RBF Neural network and ANFIS-based short-term load forecasting approach in real-time price environment
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Very short-term load forecasting: wavelet neural networks with data pre-filtering
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Using smart meter data to improve the accuracy of intraday load forecasting considering customer behavior similarities
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Short-term forecasting of electric loads using nonlinear autoregressive artificial neural networks with exogenous vector inputs
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Neural network based optimization approach for energy demand prediction in smart grid
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Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem
10.1016/j.apenergy.2018.02.131 · 2018 · External reference
Composite modeling for adaptive short-term load forecasting
10.1109/59.76686 · 1991 · External reference
Short-term load forecasting method based on fuzzy time series, seasonality and long memory process
10.1016/j.ijar.2017.01.006 · 2017 · External reference
A fuzzy inference model for short-term load forecasting
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Short-term power load forecasting using grey correlation contest modeling
10.1016/j.eswa.2011.07.072 · 2012 · External reference
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in queensland, australia
10.1016/j.aei.2017.11.002 · 2018 · External reference
Two-phase particle swarm optimized-support vector regression hybrid model integrated with improved empirical mode decomposition with adaptive noise for multiple-horizon electricity demand forecasting
10.1016/j.apenergy.2018.02.140 · 2018 · External reference
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
10.1016/j.apenergy.2014.04.016 · 2014 · External reference
Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption
10.1016/j.enbuild.2008.06.013 · 2008 · External reference
Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques
10.1016/j.apenergy.2018.12.042 · 2019 · External reference
Building’S electricity consumption prediction using optimized artificial neural networks and principal component analysis
10.1016/j.enbuild.2015.09.002 · 2015 · External reference
A hybrid model approach for forecasting future residential electricity consumption
10.1016/j.enbuild.2015.09.033 · 2016 · External reference
Deep belief network based hybrid model for building energy consumption prediction
10.3390/en11010242 · 2018 · External reference
Evolutionary deep learning-based energy consumption prediction for buildings
10.1109/access.2018.2887023 · 2019 · External reference
Forecasting energy consumption of multi-family residential buildings using support vector regression: investigating the impact of temporal and spatial monitoring granularity on performance accuracy
10.1016/j.apenergy.2014.02.057 · 2014 · External reference
Time series forecasting for building energy consumption using weighted support vector regression with differential evolution optimization technique
10.1016/j.enbuild.2016.05.028 · 2016 · External reference
Big data mining of energy time series for behavioral analytics and energy consumption forecasting
10.3390/en11020452 · 2018 · External reference
Energy consumption prediction for electric vehicles based on real-world data
10.3390/en8088573 · 2015 · External reference
The efficiency and distributional effects of alternative residential electricity rate designs
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Forecasting energy consumption in taiwan using hybrid nonlinear models
10.1016/j.energy.2009.04.026 · 2009 · External reference
Time series models (grey-Markov, grey model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in india
10.1016/j.energy.2009.12.021 · 2010 · External reference
Greek long-term energy consumption prediction using artificial neural networks
10.1016/j.energy.2009.10.018 · 2010 · External reference
Annual electricity consumption forecasting by neural network in high energy consuming industrial sectors
10.1016/j.enconman.2008.01.035 · 2008 · External reference
Multifactor-influenced energy consumption forecasting using enhanced back-propagation neural network
10.1016/j.energy.2017.03.094 · 2017 · External reference
Using a novel multi-variable grey model to forecast the electricity consumption of shandong province in china
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Forecasting the annual electricity consumption of turkey using an optimized grey model
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Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm
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ARIMA Forecasting of primary energy demand by fuel in turkey
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Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis
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Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model
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Forecasting energy consumption using a grey model improved by incorporating genetic programming
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Electricity consumption probability density forecasting method based on lasso-quantile regression neural network
10.1016/j.apenergy.2018.10.061 · 2019 · External reference
Interval forecasting of electricity demand: a novel bivariate emd-based support vector regression modeling framework
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Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
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Applying support vector machines to predict building energy consumption in tropical region
10.1016/j.enbuild.2004.09.009 · 2005 · External reference
Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector
10.1016/j.apenergy.2006.09.012 · 2008 · External reference
Modeling output energy based on fossil fuels and electricity energy consumption on dairy farms of iran: application of adaptive neural-fuzzy inference system technique
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Association rule mining based quantitative analysis approach of household characteristics impacts on residential electricity consumption patterns
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Electric vehicle charging demand forecasting model based on big data technologies
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Energy consumption control automation using artificial neural networks and adaptive algorithms: proposal of a new methodology and case study
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Neural network predictive control of upfc for improving transient stability performance of power system
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Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting
10.1016/j.apenergy.2017.04.039 · 2017 · External reference
Towards smart distribution grids: A structured market engineering review
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Behavioral studies in energy economics: a review and research framework
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Unresolved reference
External reference
Unresolved reference
External reference
Open data for electricity modeling: Legal aspects
10.1016/j.esr.2019.100433 · 2020 · External reference
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