Machine Learning and Deep Learning in Energy Systems: A Review (2022-04-18T00:00:00.000000Z)

TL;DR

A comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence for use in energy systems.

Abstract

With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

Authors

Mohammad Mahdi Forootan

1 papers

I. Larki

1 papers

Rahim Zahedi

1 papers

References268 items

Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings

TL;DR

Abstract

Authors

References268 items

Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings

Electricity price forecasting on the day-ahead market using machine learning

Time-series aggregation for the optimization of energy systems: Goals, challenges, approaches, and opportunities

Short term electricity price forecasting using a new hybrid model based on two-layer decomposition technique and ensemble learning

Evaluation of the concentration of suspended particles in underground subway stations in Tehran and its comparison with ambient concentrations

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

Explaining household electricity consumption using quantile regression, decision tree and artificial neural network

Evaluation of Resources and Potential Measurement of Wind Energy to Determine the Spatial Priorities for the Construction of Wind-Driven Power Plants in Damghan City

Strategic Study for Renewable Energy Policy, Optimizations and Sustainability in Iran

Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM

Reduction of the environmental impacts of the hydropower plant by microalgae cultivation and biodiesel production.

Automated classification of power quality disturbances in a SOFC&PV-based distributed generator using a hybrid machine learning method with high noise immunity

Machine learning analysis of electric arc furnace process for the evaluation of energy efficiency parameters

Investigating the hydropower plants production and profitability using system dynamics approach

A meta-learning classification model for supporting decisions on energy efficiency investments

Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system

Forecasting oil prices: New approaches

Potential measurement of Iran's western regional wind energy using GIS

Generative Adversarial Networks

Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm

Automatic fault classification in photovoltaic modules using Convolutional Neural Networks

Economic, Environmental and Social Impact of Carbon Tax for Iran: A Computable General Equilibrium Analysis

Clustering analysis of typical scenarios of island power supply system by using cohesive hierarchical clustering based K-Means clustering method

An optimized algorithm for optimal power flow based on deep learning

Effective forecasting of key features in hospital emergency department: Hybrid deep learning-driven methods

A self-adaptive deep learning algorithm for intelligent natural gas pipeline control

Real-time power prediction approach for turbine using deep learning techniques

Energy, exergy, exergoeconomic and exergoenvironmental analysis and optimization of quadruple combined solar, biogas, SRC and ORC cycles with methane system

Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

Energy management of hybrid energy system sources based on machine learning classification algorithms

Prediction of residential district heating load based on machine learning: A case study

A multivariate grey prediction model based on energy logistic equation and its application in energy prediction in China

Numerical and experimental simulation of gas-liquid two-phase flow in 90-degree elbow

Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA)

Autoencoder-based anomaly root cause analysis for wind turbines

A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique

An improved deep belief network based hybrid forecasting method for wind power

A review and taxonomy of wind and solar energy forecasting methods based on deep learning

Imaging time-series with features to enable visual recognition of regional energy consumption by bio-inspired optimization of deep learning

Machine learning models to quantify the influence of PM10 aerosol concentration on global solar radiation prediction in South Africa

Point and interval forecasting of electricity supply via pruned ensembles

Corrosion detection and severity level prediction using acoustic emission and machine learning based approach

A novel optimization method combining metaheuristics and machine learning for daily optimal operations in building energy and storage systems

Investigation of correlation between chemical composition and properties of biodiesel using principal component analysis (PCA) and artificial neural network (ANN)

Hybrid wind speed forecasting model based on multivariate data secondary decomposition approach and deep learning algorithm with attention mechanism

A Deep Neural Network based model for the prediction of Hybrid Electric Vehicles carbon dioxide emissions

Predicting city-scale daily electricity consumption using data-driven models

Ensemble machine learning paradigms in hydrology: A review

Electrical load-temperature CNN for residential load forecasting

Machine learning and data mining in manufacturing

Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries

Battery production design using multi-output machine learning models

Hybrid deep neural model for hourly solar irradiance forecasting

Applications of reinforcement learning in energy systems

A deep learning framework for building energy consumption forecast

Data driven occupancy information for energy simulation and energy use assessment in residential buildings

Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems

Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models

Reinforcement learning based automated history matching for improved hydrocarbon production forecast

Ensemble learning for intrusion detection systems: A systematic mapping study and cross-benchmark evaluation

Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast

Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network

Federated learning for machinery fault diagnosis with dynamic validation and self-supervision

Fault detection based on Bayesian network and missing data imputation for building energy systems

Prediction of daily global solar radiation using different machine learning algorithms: Evaluation and comparison

Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation

A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

Prediction-based analysis on power consumption gap under long-term emergency: A case in China under COVID-19

Forecasting household electric appliances consumption and peak demand based on hybrid machine learning approach

Faults in smart grid systems: Monitoring, detection and classification

Study on power consumption load forecast based on K-means clustering and FCM–BP model

Early detection and localization of stator inter-turn faults based on discrete wavelet energy ratio and neural networks in induction motor

Neural network prediction of parameters of biomass ashes, reused within the circular economy frame

Machine learning for site-adaptation and solar radiation forecasting

A self-adaptive kernel extreme learning machine for short-term wind speed forecasting

Line-line fault detection and classification for photovoltaic systems using ensemble learning model based on I-V characteristics