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Data analytics in the electricity market: a systematic literature review

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Abstract

In the last decade, data analytics studies have covered a wide range of fields across the entire value chain in the electricity sector, from production and transmission to the electricity market, distribution, and load consumption. It is essential to integrate and organize the wide range of current scientific publications to effectively allow researchers and specialists to implement and progress cutting-edge methodologies in the future. Because of the electricity market’s significance in the value chain of the electricity sector, in this study, we structure a systematic literature review of the data analytics-related works following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) framework to categorize the more common applications and approaches in the electricity market field. After refining the identified studies from the Web of Science database using the inclusion and exclusion criteria, 925 articles were chosen as the final pool of literature. Investigation of the extracted studies reveals that the application of data analytics in the electricity market can be clustered into four distinct groups: Prediction, Demand Side Management (DSM), Analysis of the market power, and Market simulation. Within the categorized applications, Prediction with 67% is the most frequent application of data analytics in the electricity market, followed by market simulation (14%), analysis of the market power (9%), DSM (7%), and other applications (3%).

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Abbreviations

AI:

Artificial intelligence

ANN:

Artificial neural network

ARIMA:

Autoregressive integrated moving average

ARMA:

Autoregressive moving average

BA:

Bat algorithm

BPNN:

Backpropagation neural network

CNN:

Convolutional neural network

CS:

Cuckoo search

DNN:

Deep neural network

DSM:

Demand side management

ECNN:

Enhance CNN

ELM:

Extreme learning machines

ENN:

Enhance NN

FA:

Firefly algorithm

FFNN:

Feed forward neural networks

GARCH:

Generalized autoregressive conditional heteroskedasticity

GPR:

Gaussian process regression

GRNN:

Generalized regression neural network

GRU:

Gated recurrent units

HBMO:

Honeybee mating optimization

KELM:

Kernel extreme learning machine

KNN:

K-nearest neighbors

LR:

Linear regression

LSSVM:

Least square support vector machines

LSTM:

Long short-term memory

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MCP :

Multiple country publication

MLP:

Multilayer perceptron

MLR:

Multiple linear regression

MOE:

Merit order effect

MSE:

Mean square error

MV Reg:

Multivariate regression

NARX:

Nonlinear autoregressive network with the exogenous model

NLSSVM:

Nonparallel least square support vector machines

NN:

Neural network

OLS:

Ordinary least squares regression

PRISMA:

Preferred-reporting items for systematic-review and meta-analysis

PSO:

Particle swarm optimization

QR:

Quartile regression

R-ANN:

Rough artificial neural network

RBM:

Restricted boltzmann machine

RES:

Renewable energy sources

RF:

Random forest

Ridge Reg:

Ridge regression

RMSE:

Root mean square error

RNN:

Recurrent neural network

SAE:

Stacked auto encoders

SAPSO:

Self-adaptive particle swarm optimization

SARIMAX:

Seasonal auto-regressive integrated moving average with exogenous factors

SCP:

Single country publication

SDE:

Standard deviation of the error

SLR:

Systematic literature review

sMAPE:

Symmetric mean absolute percentage error

SOM:

Self-organizing map

SVM:

Support vector machines

SVR:

Support vector regression

VMD:

Variational mode decomposition

WT:

Wavelet transform

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  1. Department of Energy, Politecnico di Torino, Corso Duca degli Abruzzi, 24, 10129, Torino, Italy

    Mahmood Hosseini Imani, Ettore Bompard, Pietro Colella & Tao Huang

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Imani, M.H., Bompard, E., Colella, P. et al. Data analytics in the electricity market: a systematic literature review. Energy Syst (2023). https://doi.org/10.1007/s12667-023-00576-1

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