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Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches

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Abstract

The growing need for energy from renewable sources, along with the unpredictable nature of wind power, has necessitated the development of efficient Wind Power Forecasting (WPF) algorithms. This study addresses the pressing issue of enhancing WPF algorithms in response to the growing demand for renewable energy and the inherent unpredictability of wind power. Over seven years from 2016 to 2023, conducted an exhaustive analysis of 92 research papers, focusing on the integration of Artificial Intelligence (AI) technologies to develop a robust WPF system. The study employs various AI approaches, including Deep Learning (DL), Machine Learning (ML), and neural networks, to predict wind energy generation with higher precision. Our main findings highlight a significant improvement in prediction accuracy, with the AI-based WPF system outperforming traditional methods by an average of 15%, based on a cross-validation of historical data. The integration of AI enables real-time adaptation to changing weather patterns, resulting in a 20% increase in responsiveness compared to conventional forecasting. Moreover, the proposed system optimizes resource allocation, leading to a 10% increase in energy efficiency and improved grid integration. Our results underscore the potential of AI in revolutionizing WPF, offering tangible advancements in accuracy, responsiveness, and resource efficiency. These findings advocate for the widespread adoption of AI-driven WPF systems to enhance the reliability and performance of renewable energy systems, contributing significantly to the global transition towards sustainable energy.

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Data Availability

Not Applicable.

Abbreviations

ANFIS:

Adaptive Neuro Fuzzy Inference System

AI:

Artificial Intelligence

ANN:

Artificial Neural Network

ARDA:

Autoregressive Dynamic Adaptive

ARIMA:

Autoregressive Integrated Moving Average

ARIMAX:

Autoregressive Integrated Moving Average with Exogenous Variable

ARMA:

Auto Regressive Moving Average

BP:

Backpropagation

Bi-GRU:

Bidirectional Gated Recurrent Unit

Bi-LSTM:

Bidirectional Long Short-Term Memory

CSO:

Chicken Swarm Optimization

CEEMD:

Complementary Ensemble Empirical Mode Decomposition

CFD:

Computational Fluid Dynamics

CNN:

Convolutional Neural Network

DBN:

Deep Belief Networks

DL:

Deep Learning

ECMWF:

European Centre for Medium-Range Weather Forecasts

GRU:

Gated Recurrent Unit

GPR:

Gaussian Process Regression

GP:

Gaussian Processes

GA:

Genetic Algorithm

GRAPES-MESO:

Global/Regional Assimilation and Prediction System for Mesoscale Meteorology

IEEE:

Institute of Electrical and Electronics Engineers

K.A. CARE:

King Abdullah City for Atomic and Renewable Energy

LS-SVM:

Least Squares Support Vector Machine

LSTM:

Long Short-Term Memory

LUBE:

Lower Upper Bound Estimation

ML:

Machine Learning

MAE:

Mean Absolute Error

MAPE:

Mean Absolute Percentage Error

MSE:

Mean Square Error

MRN:

Multi-level Residual Network

MOOFADA:

Multi-Objective Optimization Framework for Adaptive Design and Analysis

NREL:

National Renewable Energy Laboratory

NWP:

Numerical Weather Prediction

NWP:

Numerical Weather Prediction

PSO:

Particle Swarm Optimization

PSO-SVR:

Particle Swarm Optimization—Support Vector Regression

PSR-BiGRU:

Phase Space Reconstruction—Bidirectional Gated Recurrent Unit

RNN:

Recurrent Neural Network

RMSE:

Root Mean Square Error

STSR-LSTM:

Sequence-to-Sequence Long-Short Term Memory

SVM:

Support Vector Machine

VMD:

Variational Modal Decomposition

WTD:

Wavelet Threshold Denoising

WPF:

Wind Power Forecasting

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Guru Jambheshwar University of Science and Technology, Hisar, Haryana, 125001, India

    Krishan Kumar, Priti Prabhakar, Sumit Saroha & Kalyan Singh

  2. Department of Instrumentation Engineering, Kurukshetra University, Kurukshetra, Haryana, India

    Avnesh Verma

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  1. Krishan Kumar
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Kumar, K., Prabhakar, P., Verma, A. et al. Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18916-3

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