Energy production predication via Internet of Thing based machine learning system

Filho, Pedro Pedrosa Rebouças; Gomes, Samuel Luz; Nascimento, Navar de Medeiros Mendonça e; Medeiros, Cláudio Marques de Sá; Outay, Fatma; Albuquerque, Victor Hugo C. de

doi:10.1016/j.future.2019.01.020

Cited by 15 publications

(9 citation statements)

References 35 publications

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“…According to Table 5, Brazil (n = 16) is the Latin American country that has published the most articles related to ML in generating renewable energies, followed by Colombia (n = 5). Concerning Brazil, one of their articles addresses the prediction of wind energy production through ML based on the Internet of Things (Rebouças Filho et al, 2019). In turn, these propose the design of a low-cost system based on the Internet of Things for monitoring climate variables that affect the generation of photovoltaic energy (Melo et al, 2021).…”

Section: Bibliometric Indicatorsmentioning

confidence: 99%

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Hernandez-Palma,

Alvarado,

Guiliany

et al. 2024

IJEEP

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The global energy industry fundamentally transformed towards renewable energy sources, driven by the sustainability paradigm. This shift was crucial in addressing the challenges of climate change and resource scarcity. Machine Learning (ML) played a pivotal role in enhancing the efficiency and reliability of renewable energy systems. This study conducted a comprehensive analysis of scientific production at the intersection of ML and renewable energy generation, focusing on Latin America. Employing a methodology based on documentary research and bibliometric processes, utilizing the Scopus database with the support of R and VOSviewer software, our research revealed a significant increase in interest and investment in research related to ML and renewable energies since 2020. This exponential growth scenario in this knowledge area had significant implications for Latin America and the world, fostering technological advancements and the adoption of renewable energies. Countries such as China, India, the United States, South Korea, and Saudi Arabia represented 61% of the global scientific production in this field, underscoring its global relevance. This growth indicated a growing interest and investment in ML applications in renewable energies, aligning with the 2030 Agenda for Sustainable Development. This research aligns with the Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 9 (Industry, Innovation, and Infrastructure). It contributed to progress toward a more sustainable future, benefiting society through more efficient and sustainable energy systems, the energy industry through increased innovation and the adoption of clean technologies, and Latin America, which could leverage these findings to sustainably drive its economic and environmental development.

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Section: Bibliometric Indicatorsmentioning

confidence: 99%

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Hernandez-Palma,

Alvarado,

Guiliany

et al. 2024

IJEEP

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“…This section shows works that discuss several areas related to wind power prediction. The first area covers works that predict a wind farm's energy [1, [13][14][15][16]; other works focus on wind speed prediction [17,18]. Wind turbine predictive maintenance and wind turbine health assessment [19,20] are other topics of research.…”

Section: Literature Review On Wind Power Predictionmentioning

confidence: 99%

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

2023

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The world’s technological and economic advancements have led to a sharp increase in the demand for electrical energy. Saudi Arabia is experiencing rapid economic and demographic growth, which is resulting in higher energy needs. The limits of fossil fuel reserves and their disruption to the environment have motivated the pursuit of alternative energy options such as wind energy. In order to regulate the power system to maintain safe and dependable operation, projections of current and daily power generation are crucial. Thus, this work focuses on wind power prediction and the statistical analysis of wind characteristics using wind data from a meteorological station in Makkah, Saudi Arabia. The data were collected over four years from January 2015 to July 2018. More than twelve thousand data points were collected and analyzed. Layout and wake effect studies were carried out. Furthermore, the near wake length downstream from the rotor disc between 1 and 5 rotor diameters (1D to 5D) was taken into account. Five robust machine learning algorithms were implemented to estimate the potential wind power production from a wind farm in Makkah, Saudi Arabia. The relationship between the wind speed and power produced for each season was carefully studied. Due to the variability in the wind speeds, the power production fluctuated much more in the winter. The higher the wind speed, the more significant the difference in energy production between the five farm layouts, and vice versa, whereas at a low wind speed, there was no significant difference in the power production in all of the near wake lengths of the 1D to 5D rotor diameters downstream from the rotor disc. Among the utilized prediction models, the decision tree regression was found to have the best accuracy values in all four utilized evaluation metrics, with 0.994 in R-squared, 0.025 in MAE, 0.273 in MSE, and 0.522 in RMSE. The obtained results were satisfactory and provide support for the construction of several wind farms, producing hundreds of megawatts, in Saudi Arabia, particularly in the Makkah Region.

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“…Turbine curve modeling can also be used to account for the amount of energy loss while the wind turbine is shut down for maintenance. Additionally, very shortterm forecasting is used to plan turbine control actions, while long-term forecasting allows issues to be properly handled with regard to energy marketing and reducing financial and technical risk [7]. Wind power forecasting tools are invaluable because they allow better dispatch, scheduling, unit engagement of thermal and hydroelectric generators and energy storage plants, as well as provide a more competitive market.…”

Section: Introductionmentioning

confidence: 99%

“…Using a nonlinear autoregressive neural network, the wind speed of a 5-day horizon was forecasted, and energy production was estimated. The data used were 4320 samples for training and 720 for testing [7]. Aqsa et al proposed a deep neural network both as a base-regressor and as a meta-regression with 24 time series delays.…”

Section: Introductionmentioning

confidence: 99%

“…In this work, the convolutional NN is also used, and the forecasting horizons range from 15 min to 8 h ahead. Other research approaches, in which the use of forecasting methods with very high computational complexity is privileged, are mainly based on deep learning or hybrid and compound forecasting methods [2,[5][6][7]. Most of the aforementioned works present complicated methods to forecast wind speed, and in a few of them, the power generated by wind farms is estimated.…”

Section: Introductionmentioning

confidence: 99%

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Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

2021

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Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

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Energy production predication via Internet of Thing based machine learning system

Cited by 15 publications

References 35 publications

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review

Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia

Time Series Forecasting for Wind Energy Systems Based on High Order Neural Networks

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