Abstract
Sustainable energy is the new normal towards saving the environment, thus resources generating sustainable green energy have gained global attention. Out of all the predominant sustainable energy genres, wind energy is one of the promising and growing solutions to improve efficiency towards sustainability. To expand the area of wind power generation and install more wind farms in future; accurate predictive analytics is mandatory. Due to uncertainty and stochastic nature of wind power time series parameters and outputs, enormous data driven; various machine learning and deep learning approaches have been proposed for the simulation and predictions for wind power predictive analytics. Many approaches have been working towards using Long Short Term Memory (LSTM) and its variants to improve accuracy in wind power predictions. With an aim of easing researchers and applications working in the field of wind power predictive analytics, this study strives to provide critical insights on usage LSTM and associated model in wind power predictions. This study explores at the root level; hence a survey is first made to understand and explore requirements and benefits of time series predictive analytics. Second, a generic exploration of all the different models and performance metrics used over different time series data is performed. Third, a thorough review on WP predictive analysis, based on LSTM as a whole or part of the model is presented. This will also include decomposition techniques, normalization methods, performance metrics, experimented datasets and dependent variable used for wind power predictive analytics. These approaches have been thoroughly seeking to improve the results; however certain challenges still persist due to variability and uncertain nature of wind parameters. Therefore, the major objective of presenting this paper is to learn (i) requirements and benefits of time series predictive analytics, (ii) state of art models and metrics used in time series predictive analytics, (iii) role of LSTM and associated models in wind power predictive analytics, (iv) different decomposition techniques, normalization methods, performance metrics, experimented datasets and predictive frequency used in wind power predictive analytics; and (v) challenges persisting in wind power predictive analytics and usage of LSTM.
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Abbreviations
- GBM:
-
Gradient boosting machine
- DBN:
-
Deep belief network
- IoT:
-
Internet of Things
- AWNN:
-
Adaptive wavelet neural network
- NREL:
-
National renewable energy laboratory
- EFG:
-
Enhanced forget gate
- CSO:
-
Cuckoo search optimization algorithm
- NIWE:
-
National institute of wind energy
- MASE:
-
Mean absolute scale error
- MAE:
-
Mean absolute error
- RMSPE:
-
Root mean square percentage error
- RMSE:
-
Root mean square error
- MSE:
-
Mean squared error
- MAPE:
-
Mean square percentage error
- R2 :
-
Coefficient of variation
- SATCN:
-
Self-attention temporal convolutional network
- EA:
-
Evolutionary attention
- CRS:
-
Competitive random search
- BP:
-
Back propagation
- ENN:
-
Elman neural network
- ED:
-
Encoder decoder
- GA:
-
Genetic algorithm
- TC:
-
Tropical cyclone
- FTSNN:
-
Feedback time series neural network
- IFTSNN:
-
Input feedback time series neural network
- NFMP: Networks:
-
Friends, money, and bytes
- NI: Networks:
-
Friends, money, and bytes
- KPI:
-
Key performance indicator
- RF:
-
Random forest
- BBN:
-
Bayesian belief networks
- DE:
-
Differential evolution
- WNN:
-
Wavelet neural network
- RNN:
-
Recurrent neural network
- ELM:
-
Extreme learning machine
- DWT:
-
Discrete wavelet transformation
- FFT:
-
Fast Fourier transformation
- LR:
-
Linear regression
- NWP:
-
Numerical weather prediction
- BR:
-
Bayesian ridge
- LSSVM:
-
Least squares support vector machines
- BI:
-
Business intelligence
- EL:
-
Ensemble learning
- GPR:
-
Gaussian process regression
- SVR:
-
Support vector regression
- BO:
-
Bayesian optimization
- CPCB:
-
Central pollution control board
- NCRB:
-
National crime records bureau
- MFFNN:
-
Multilayer feed-forward neural network
- TDNN:
-
Time-delay neural network
- RBFNN:
-
Radial basis function neural networks
- GRU:
-
Gated recurrent unit
- CNN:
-
Convolution neural network
- PSBF:
-
Pattern sequence based forecasting
- MLP:
-
MultiLayer perceptron
- VAE:
-
Variational AutoEncoder
- VAR:
-
Vector autoregression
- GWEC:
-
Global wind energy council
- PSO:
-
Particle swarm optimization
- SOA:
-
Swarm optimization algorithm
- ACO:
-
Ant colony optimization
- BSO:
-
Brain storm optimization
- DE:
-
Differential evolution
- MWdc:
-
MegaWatts defined conditions
- LSSVM:
-
Least-squares support vector machines
- MTL:
-
Multitask learning
- GMM:
-
Gaussian mixture model
- MFO:
-
Moth-flame optimization
- CSO:
-
Cuckoo search algorithm
- ABC:
-
Artificial bee colony
- FA:
-
Firefly algorithm
- DE:
-
Differential evolution
- RMT:
-
R-matrix with time
- MIMO:
-
Multiple input and multiple output
- DFF:
-
Deep feed forward
- EMD:
-
Empirical mode decomposition
- PSR:
-
Phase space reconstruction
- AR:
-
Autoregressive
- ARMA:
-
Autoregressive moving average
- ARIMA:
-
Autoregressive integrated moving average
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Garg, S., Krishnamurthi, R. A survey of long short term memory and its associated models in sustainable wind energy predictive analytics. Artif Intell Rev 56 (Suppl 1), 1149–1198 (2023). https://doi.org/10.1007/s10462-023-10554-9
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DOI: https://doi.org/10.1007/s10462-023-10554-9