Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Khosravi, Ali; Machado, Luiz; Nunes, Raphael

doi:10.1016/j.apenergy.2018.05.043

Cited by 183 publications

(79 citation statements)

References 47 publications

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“…Accurate forecasting of WPREs is necessary for the efficient integration of a wind farm into an electricity system [103]. Khosravi et al (2018) [99] developed models based on a group model of data handling type neural network, adaptive neuro-fuzzy inference system, ANFIS optimized with an ant colony, ANFIS optimized with particle swarm optimization algorithm, ANFIS optimized with genetic algorithm, and multilayer feed-forward neural network. Day, month, average air temperature, minimum and maximum air temperature, air pressure, wind speed, relative humidity, latitude, longitude, and top of atmosphere insolation.…”

Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

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Section: (A)mentioning

confidence: 99%

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

et al. 2019

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“…In other words, future values are predicted by learning the previous data series. From this perspective, MLP-ANN is introduced to predict time series because this method yields more satisfactory results in some recent studies [41].…”

Section: Predicting Weather Parameters Using Time Series Ff-annmentioning

confidence: 99%

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

et al. 2020

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In recent years, taking advantage of renewable energy sources (RESs) has increased considerably due to their unique capabilities, such as a flexible nature and sustainable energy production. Prosumers, who are defined as proactive users of RESs and energy storage systems (ESSs), are deploying economic opportunities related to RESs in the electricity market. The prosumers are contracted to provide specific power for consumers in a neighborhood during daytime. This study presents optimal scheduling and operation of a prosumer owns RESs and two different types of ESSs, namely stationary battery (SB) and plugged-in electric vehicle (PHEV). Due to the intermittent nature of RESs and their dependency on weather conditions, this study introduces a weather prediction module in the energy management system (EMS) by the use of a feed-forward artificial neural network (FF-ANN). Linear regression results for predicted and real weather data have achieved 0.96, 0.988, and 0.230 for solar irradiance, temperature, and wind speed, respectively. Besides, this study considers the depreciation cost of ESSs in an objective function based on the depth of charge (DOD) reduction. To investigate the effectiveness of the proposed strategy, predicted output and the real power of RESs are deployed, and a mixed-integer linear programming (MILP) model is used to solve the presented day-ahead optimization problem. Based on the obtained results, the predicted output of RESs yields a desirable operation cost with a minor difference (US$0.031) compared to the operation cost of the system using real weather data, which shows the effectiveness of the proposed EMS in this study. Furthermore, optimum scheduling with regard to ESSs depreciation term has resulted in the reduction of operation cost of the prosumer and depreciation cost of ESS in the objective function has improved the daily operation cost of the prosumer by $0.8647.

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“…Based on the nonlinear learning integration of LSTM, SVRM and EO, the proposed LSTM system achieved satisfactory wind speed prediction performance. In [18], machine learning algorithm was used to predict the wind speed of Osorio wind farm. The optimal intelligence model was introduced at different time intervals.…”

Section: Literature Reviewmentioning

confidence: 99%

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

Guo

Jiang²,

Jiang³

et al. 2019

J Supercomput

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In recent years, bridge health monitoring system has been widely used to deal with massive data produced with the continuous growth of monitoring time. However, how to effectively use these data to comprehensively analyze the state of a bridge and provide early warning of bridge structure changes is an important topic in bridge engineering research. This paper utilizes two algorithms to deal with the massive data, namely Kohonen neural network and long short-term memory (LSTM) neural network. The main contribution of this study is using the two algorithms for health state evaluation of bridges. The Kohonen clustering method is shown to be effective for getting classification pattern in normal operating condition and is straightforward for outliers detection. In addition, the LSTM prediction method has an excellent prediction capability which can be used to predict the future deflection values with good accuracy and mean square error. The predicted deflections agree with the true deflections, which indicate that the LSTM method can be utilized to obtain the deflection value of structure. What's more, we can observe the changing trend of bridge structure by comparing the predicted value with its limit value under normal operation.

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Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil

Cited by 183 publications

References 47 publications

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Day-Ahead Optimization of Prosumer Considering Battery Depreciation and Weather Prediction for Renewable Energy Sources

Data mining algorithms for bridge health monitoring: Kohonen clustering and LSTM prediction approaches

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