A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Al-Zadjali, Saira; Maashri, Ahmed Al; Al-Hinai, Amer; Abri, Rashid Al; Gajare, Swaroop; Al-Yahyai, Sultan; Bakhtvar, Mostafa

doi:10.3390/en14237878

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…As a result, the MLR model attained an average accuracy of 92.3% based on the coefficient of determination (𝑅 2 ) between predicted and actual results, which is greater than other methods such as ridge regression (87.2%), convolutional neural network (82.0%) and hubber regression (9.19%) [20]. Al-Zadjali et al, [21] demonstrate an ANN model capable of predicting wind speed based on temperature, humidity, and air pressure, with a correlation of 98.73% between predicted and measured wind speed. In [22], it is demonstrated that RNN has a superior performance for wind speed nowcast prediction than a standard ANN model, as measured by a smaller RMSE between predicted and actual wind speeds.…”

Section: Wind Speed Prediction Using Linear Regression Model and Ann ...mentioning

confidence: 89%

Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

2023

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Section: Wind Speed Prediction Using Linear Regression Model and Ann ...mentioning

confidence: 89%

Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

2023

ARFMTS

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“…The literature [23] outlines modern energy system modeling, and current user-side prediction modeling mostly focuses on power prediction. Wind power generation and solar power generation are forecasted in the literature [24][25][26] by developing an accurate power prediction model.…”

Section: Introductionmentioning

confidence: 99%

Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

et al. 2021

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This research proposes an optimization technique for an integrated energy system that includes an accurate prediction model and various energy storage forms to increase load forecast accuracy and coordinated control of various energies in the current integrated energy system. An artificial neural network is utilized to create an accurate short-term load forecasting model to effectively predict user demand. The 0–1 mixed integer linear programming approach is used to analyze the optimal control strategy for multiple energy systems with storage, cold energy, heat energy, and electricity to solve the problem of optimal coordination. Simultaneously, a precise load forecasting method and an optimal scheduling strategy for multienergy systems are proposed. The equipment scheduling plan of the integrated energy system of gas, heat, cold, and electricity is proposed after researching the operation characteristics and energy use process of the equipment in the combined power supply system. A system economic operation model is created with profit maximization in mind, while also taking into account energy coordination between energy and the power grid. The rationality of the algorithm and model is verified by analyzing the real data of a distributed energy station in Wuhan for two years.

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Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

et al. 2022

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Power generation forecasts for wind farms, especially with a short-term horizon, have been extensively researched due to the growing share of wind farms in total power generation. Detailed forecasts are necessary for the optimization of power systems of various sizes. This review and analytical paper is largely focused on a statistical analysis of forecasting errors based on more than one hundred papers on wind generation forecasts. Factors affecting the magnitude of forecasting errors are presented and discussed. Normalized root mean squared error (nRMSE) and normalized mean absolute error (nMAE) have been selected as the main error metrics considered here. A new and unique error dispersion factor (EDF) is proposed, being the ratio of nRMSE to nMAE. The variability of EDF depending on selected factors (size of wind farm, forecasting horizons, and class of forecasting method) has been examined. This is unique and original research, a novelty in studies on errors of power generation forecasts in wind farms. In addition, extensive quantitative and qualitative analyses have been conducted to assess the magnitude of forecasting error depending on selected factors (such as forecasting horizon, wind farm size, and a class of the forecasting method). Based on these analyses and a review of more than one hundred papers, a unique set of recommendations on the preferred content of papers addressing wind farm generation forecasts has been developed. These recommendations would make it possible to conduct very precise benchmarking meta-analyses of forecasting studies described in research papers and to develop valuable general conclusions concerning the analyzed phenomena.

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A Fast and Accurate Wind Speed and Direction Nowcasting Model for Renewable Energy Management Systems

Cited by 3 publications

References 23 publications

Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

Analysis of Wind Speed Prediction using Artificial Neural Network and Multiple Linear Regression Model using Tinyml on Esp32

Optimized Operation of Integrated Energy Microgrid with Energy Storage Based on Short-Term Load Forecasting

Evaluation Metrics for Wind Power Forecasts: A Comprehensive Review and Statistical Analysis of Errors

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