A Review of Intelligent Systems for the Prediction of Wind Energy Using Machine Learning

Dubey, Ashutosh Kumar; Kumar, Abhishek; Ramírez, Isaac Segovia; Márquez, Fausto Pedro Garcı́a

doi:10.1007/978-3-031-10388-9_35

Cited by 3 publications

(1 citation statement)

References 52 publications

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“…It cannot be said that every applied technique is fit for all prediction conditions. Energy consumption forecasting by machine learning techniques has been employed in many fields such as country primary energy and secondary energy, electricity markets based on renewable energy [3,4], smart grids [5][6][7], intelligent communities [8], office buildings [9], and CO 2 emissions [10]. In this work, the country energy demand prediction based on machine learning techniques will be mainly discussed, and the literature review is listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine

Liu

2022

Processes

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The long-term demand forecast for annual national electricity and energy consumption plays a vital role in future strategic planning, power system installation programming, energy investment planning, and next-generation unit construction. Three machine learning algorithms of BP-NN, MLR, and LS-SVM were chosen for training forecasting models, with the data on population, GDP, mean temperature, sunshine, rainfall, and frost days in 1993–2019 serving as the input variables. The total data were divided by 70% into the training set (1993–2011) and 30% into the test set (2012–2019), in chronological order. RMSE, MAPE, and MaxError were adopted as the performance criteria. The statistical results show that the gross population of the UK increases year by year from 1993 to 2020. The GDP generally increases before 2007 but has a decline, and then varies with a large amplitude afterward. The electricity and energy consumption of the UK generally increase from 1993 and reach a peak around 2005. Afterward, a decline occurs basically year by year until 2019. The simulation results reveal that all three models predict well on the training set but have some overestimation on the test set. The LS-SVM model has the best forecasting performance among the three models on the training set. The results show that it is feasible to use machine learning algorithms to predict the future electricity and energy consumption of a country based on past economic and livelihood data. In this way, economic decision-makers can rely on the predicted values to make a well-founded layout for future energy construction and investment to avoid waste or a shortage of resources.

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Section: Introductionmentioning

confidence: 99%

Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine

Liu

2022

Processes

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A Machine Learning Forecast of Renewable Solar Power Generation and Analysis of Distribution and Management Using IOT-Based Sensor Data

Sharma,

Gongada,

Anand

et al. 2024

Lecture Notes in Networks and Systems

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Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

Rushdi,

Yoshida,

Watanabe

et al. 2024

Energies

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Wind–solar towers are a relatively new method of capturing renewable energy from solar and wind power. Solar radiation is collected and heated air is forced to move through the tower. The thermal updraft propels a wind turbine to generate electricity. Furthermore, the top of the tower’s vortex generators produces a pressure differential, which intensifies the updraft. Data were gathered from a wind–solar tower system prototype developed and established at Kyushu University in Japan. Aiming to predict the power output of the system, while knowing a set of features, the data were evaluated and utilized to build a regression model. Sensitivity analysis guided the feature selection process. Several machine learning models were utilized in this study, and the most appropriate model was chosen based on prediction quality and temporal criteria. We started with a simple linear regression model but it was inaccurate. By adding some non-linearity through using polynomial regression of the second order, the accuracy increased considerably sufficiently. Moreover, deep neural networks were trained and tested to enhance the power prediction performance. These networks performed very well, having the most powerful prediction capabilities, with a coefficient of determination R2=0.99734 after hyper-parameter tuning. A 1-D convolutional neural network achieved less accuracy with R2=0.99647, but is still considered a competitive model. A reduced model was introduced trading off some accuracy (R2=0.9916) for significantly reduced data collection requirements and effort.

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A Review of Intelligent Systems for the Prediction of Wind Energy Using Machine Learning

Cited by 3 publications

References 52 publications

Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine

Annual Electricity and Energy Consumption Forecasting for the UK Based on Back Propagation Neural Network, Multiple Linear Regression, and Least Square Support Vector Machine

A Machine Learning Forecast of Renewable Solar Power Generation and Analysis of Distribution and Management Using IOT-Based Sensor Data

Deep Learning Approaches for Power Prediction in Wind–Solar Tower Systems

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