Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review

Madasthu, Santhosh; Venkaiah, Chintham; Kumar, D. M. Vinod

doi:10.1002/eng2.12178

Cited by 89 publications

(39 citation statements)

References 88 publications

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“…The AD-PSO-Guided WOA algorithm' complexity analysis is presented in this section based on Algorithm (1). Using population number indicated as n iterations number as M t , the complexity can be defined for each part of the algorithm as…”

Section: Proposed Algorithm Complexity Analysismentioning

confidence: 99%

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Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

et al. 2021

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The development and deployment of an effective wind speed forecasting technology can improve the stability and safety of power systems with significant wind penetration. However, due to the wind's unpredictable and unstable qualities, accurate forecasting of wind speed and power is extremely challenging. Several algorithms were proposed for this purpose to improve the level of forecasting reliability. A common method for making predictions based on time series data is the long short-term memory (LSTM) network. This paper proposed a machine learning algorithm, called adaptive dynamic particle swarm algorithm (AD-PSO) combined with guided whale optimization algorithm (Guided WOA), for wind speed ensemble forecasting. The proposed AD-PSO-Guided WOA algorithm selects the optimal hyperparameters value of the LSTM deep learning model for forecasting purposes of wind speed. In experiments, a wind power forecasting dataset is employed to predict hourly power generation up to forty-eight hours ahead at seven wind farms. This case study is taken from the Kaggle Global Energy Forecasting Competition 2012 in wind forecasting. The results demonstrated that the AD-PSO-Guided WOA algorithm provides high accuracy and outperforms a number of comparative optimization and deep learning algorithms. Different tests' statistical analysis, including Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), confirms the accuracy of proposed algorithm.

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Section: Proposed Algorithm Complexity Analysismentioning

confidence: 99%

“…Wind energy is one of the essential low-carbon energy technolo-gies. It can deliver a long-term energy supply and serves as a core component for micro-grids as part of intelligent grid architecture [1]. However, wind power generation is stochastic and intermittent, posing several hurdles to its widespread adoption.…”

Section: Introductionmentioning

confidence: 99%

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

et al. 2021

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“…Accurately prediction of the wind power is challenging due to the intermittency of the speed of the wind over time. To enhance the forecasting accuracy in the long/short-term, various dynamic ANN-based techniques, such as CNN, RNN (with multi-variable, such as wind direction/speed, ambient temperature, solar irradiance, environment humidity, and air pressure [101]) have been proposed [102].…”

Section: Wind Power Predictionmentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

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This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

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“…In other words, while some approaches can forecast long-term wind speed, some others are only suitable for short-term predictions. Consequently, based on the prediction method, different time intervals are defined, ranging from seconds to years [32,83,84]. As far as wind speed varies on all time scales, that is, seconds, minutes, hours, days, months, and years, irrespective of the chosen duration of a time interval, the time intervals' duration are chosen somehow that can be categorised into four main groups of very short-term (predicting from a few seconds to a few minutes), short-term (predicting from a few minutes to a few hours), medium-term (predicting from a few hours to a month ahead) and long-term (predicting more than a month ahead).…”

Section: Wind Speed Variationsmentioning

confidence: 99%

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

et al. 2021

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Renewable Energy Sources (RES) have drawn significant attention in the past years to make the transition towards low carbon emissions. On the one hand, the intermittent nature of RES, resulting in variable power generation, hinders their high-level penetration in the power system. On the other hand, RES can aid not only to supply much more eco-friendly energy but also it allows the power system to enhance its stability by ancillary service provision. This article reviews the challenges related to the most intermittent RES utilised in Belgium, that is, wind energy and solar energy. Additionally, wind speed and solar irradiance variations, which are the cause of wind and solar intermittency, are studied. Then, recent techniques to forecast their changes, and approaches to accommodate or mitigate their impacts on the power system, are discussed. Finally, the latest statistics and future situation of RES in the Belgian power system are evaluated.

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Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review

Cited by 89 publications

References 88 publications

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System

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