Wind power forecasting based on time series model using deep machine learning algorithms

Patil, Chandrashekhar K.; Manoharan, Anto Merline; Ghosh, Aritra; Sumithra, M. G.; Karthick, Alagar; Rahim, Robbi; Arun, K. S.

doi:10.1016/j.matpr.2021.03.728

Cited by 22 publications

(15 citation statements)

References 60 publications

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“…An innovative deep-interval prediction model representing a Long LSTM proof is created to assess and contrast the existing methodologies with the proposed model. Loss functions are tuned for root average square backpropagation, and comparison tests are carried out [62].…”

Section: Wind Energy Generation and Predictionmentioning

confidence: 99%

“…Wind energy parameters have been used to investigate the relationship between initial relative energy and wind energy characteristics. Wind speed [53][54][55][56][57][58][59][61][62][63], wind direction [53][54][55][56][57]59,62,63], air density, and weather conditions like temperature and humidity [55][56][57][61][62][63] are among the parameters involved in the operation of wind farms. The table above uses various parameters to examine the performance of the models.…”

Section: Wind Energy Generation and Predictionmentioning

confidence: 99%

See 1 more Smart Citation

A Review for Green Energy Machine Learning and AI Services

Mehta,

Xu,

Lim

et al. 2023

Energies

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There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.

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Section: Wind Energy Generation and Predictionmentioning

confidence: 99%

Section: Wind Energy Generation and Predictionmentioning

confidence: 99%

A Review for Green Energy Machine Learning and AI Services

Mehta,

Xu,

Lim

et al. 2023

Energies

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“…[ 166 ] Mathematically, the temperature is proportional to pressure and thus affects the power estimation. Temperature value [ 167 ] and sensible heat flux at the surface [ 168 ] affect the WPF performance.…”

Section: Requirements and Technical Challengesmentioning

confidence: 99%

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

et al. 2022

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Wind energy generation is a fast developing efficient renewable source widely used for modern power generation. [1] Countries and energy companies have increased their interest in electricity generation from wind energy in the last decades, thanks to its natural, cost-free, and environmentally friendly features. [2] Wind power plants can generate electrical energy at any time of the day and are distributed energy sources for systems that need permanent energy. [3] Nonetheless, due to the disordered nature of the earth's atmosphere, there is a major uncertainty in the electrical power produced by the wind, which causes complexities in the utilization and management of wind energy sources. [4] The changes in weather conditions depending on time and location also create uncertainty in managing the supply-demand balance controlled by the grid operators. [5] In cases where the electrical energy produced by wind power plants is insufficient, auxiliary energy sources provide operational support to the electric grid. [6] Ensuring the supply-demand balance in the electricity grid is also an important condition for the electrical power quality and energy supply in the transmission system, and wind power plant operators care about the follow-up of wind power in terms of economy, operation, and planning. [7] For this, the presence of wind power forecasting (WPF) is an essential factor in the optimization of wind power plants. [8] For consumers to use electrical energy uninterruptedly, efficiently, and economically, wind power plant operators need to estimate the generated electrical energy smoothly. [9] The smooth estimation of electrical power generated by wind turbines increases the operational efficiency for system maintenance, planning, investment, and energy traders. [10,11] It is a precious procedure for electrical grids to provide a respectable commercial yield on the electrical energy market. [12] The capability of advanced WPF provides the contributions like balancing the power system and mitigation of penalties, scheduling the maintenance of wind power plants, investment regarding site location, and technoeconomic and socioeconomic benefits. [13,14] In this way, it is possible to reduce the energy cost and obtain high revenue in the energy market as a result of the contributions obtained for wind power generation systems. [15]

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“…Since the power of one photovoltaic module is not sufficient for electronic devices, a series connection of several cells is used to obtain the required voltage. In terms of current strength, this is increased by the parallel combination of a certain number of assemblies [11].The power generation in semiconductors is directly dependent on the amount of solar energy, so not only are the photocells installed outside, they also attempt to direct their surface perpendicular to the incident rays [12]. To protect the cells from mechanical damage and atmospheric impacts, they are mounted on a solid foundation and protected by glass from above.…”

Section: Literature Reviewmentioning

confidence: 99%

“…But chemical exposure to carbon dioxide when carbonated water is exposed to sunlight has been shown to contain harmful chemical molecules. When researchers further researched this, they studied the release of antimony at different temperatures [11,12]. At the same time, the cost of developing and installing a water heater is low [13].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy

Bharathi

Sripriya

Muthuraj³

et al. 2022

International Journal of Photoenergy

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Currently, we are trying to get electricity in alternative ways. Many solar powered water heaters have come up to use water heaters. However, these tools are not 100 percent fully effective. The device we have manufactured is an automatic device that runs in the direction of sunlight. The device runs automatically in the morning facing east and in the evening facing west. In this instrument, the defective one-inch tube lamp and the three-quarter-inch tube lamp are put together and connected in series. In this paper, a smart deep learning model was proposed to improve the performance of the solar water heater. The gap between the tube lights is filled with methane gas, and the tube inside is filled with water. The water thus filled is heated by sunlight. Methane gas acts as a fast conductor of solar heat. An electronic control device is placed to determine the temperature of the hot water and to expel the hot water. This device can heat at least 10 liters of water in 15 minutes. Increasing the number of incandescent tube lights can heat up a large amount of water when this device is set up, or it can be designed by replacing tube lights with a series of large glass tubes using the same technology. This tool can be manufactured at low cost so that people from all walks of life can use it.

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Wind power forecasting based on time series model using deep machine learning algorithms

Cited by 22 publications

References 60 publications

A Review for Green Energy Machine Learning and AI Services

A Review for Green Energy Machine Learning and AI Services

A Review on Wind Power Forecasting Regarding Impacts on the System Operation, Technical Challenges, and Applications

Deep Learning-Based Smart Hybrid Solar Water Heater Erection Model to Extract Maximum Energy

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