Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

Renewable energy resources can be deployed locally and efficiently using the concept of microgrids. Due to the natural uncertainty of the output power of renewable energy resources, the planning for a proper operation of microgrids can be a challenging task. In addition, the information about the loads and the power consumption of them can create benefits to increase the efficiency of the microgrids. However, electrical loads can have uncertainty due to reasons such as unpredictable behavior of the consumers. To exploit a microgrid, energy management is required at the upper level of operation and control in order to reduce the costs. One of the most important tasks of the energy management system is to satisfy the loads and, in other words, develop a plan to maintain equilibrium between the power generation and power consumption. To obtain information about the output power of renewable energy resources and power consumption, deep learning can be implemented as a powerful tool, which is able to predict the desired values. In addition, weather conditions can affect the output power of renewable energy-based resources and the behavior of the consumers and, as a result, the power consumption. So, deep learning can be deployed for the anticipation of the weather conditions. This paper will study the recent works related to deep learning, which has been implemented for the prediction of the output power of renewable energy resources (i.e., PVs and wind turbines), electrical loads, and weather conditions (i.e., solar irradiance and wind speed). In addition, for possible future directions some strategies are suggested, the most important of which is the implementation of quantum computing in cyber–physical microgrids.

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“…Additionally, for more studies, some methods have been proposed by [108][109][110][111][112][113][114][115] for the prediction of the wind speed.…”

Section: Wind Speed Predictionmentioning

confidence: 99%

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

et al. 2023

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“…The detection, collection, and management of faults are critical components in ensuring the efficiency and dependability of intricate systems, such as industrial machinery, power grids, and transportation systems. However, these systems are susceptible to diverse kinds of faults and malfunctions, which can lead to expensive downtime, decreased productivity, and potentially dangerous safety risks [1][2][3][4][5][6][7]. Additionally, refs.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

Almasoudi

2023

Sustainability

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Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future.

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“…In contrast to statistical models, AI approaches are better equipped to handle the nonlinearity of wind speed.. Some of the methods investigated within machine learning models include Support Vector Machine (SVM) 21) , Decision Trees (DT) 22) , Gaussian Process Regression (GPR) 23) , and Extreme Learning Machine (ELM) 24) , and Artificial Neural Networks (ANN) 25) ANN has strong fault tolerance, real-time operation, self-learning, flexibility, and implementation ease. These structures, based on biological neurons, effectively address issues that cannot be defined analytically.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Forecasting with Weather Data using Deep Learning - Case Study in Baron Techno Park

Halidah,

Hesty,

Aji

et al. 2023

Evergreen

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In a microgrid with small-scale renewable sources, the unpredictable and highly variable nature of wind necessitates the adoption of reliable wind forecasting technologies. This study employs artificial neural networks (ANNs), specifically the Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which are classified as Deep Learning (DL) networks. These models integrate diverse weather data, such as wind speed, temperature, humidity, and atmospheric pressure, derived from actual measurements collected in Baron Techno Park, an isolated microgrid situated in the coastal region of Yogyakarta, Indonesia. For various scenarios, the root-mean-square error (RMSE) and mean absolute error (MAE) performances of the proposed ANN-based multivariable model are given and contrasted. Furthermore, it examines the impact of incorporating multiple local variables in contrast to solely relying on wind power, comparing against the persistence method. The findings reveal that the model incorporating a comprehensive set of weather data as inputs attains the lowest RMSE and MAE values. It is also can be concluded that additional weather data, even though they show almost no correlation to wind power in Baron Techno Park can improve short-term wind power prediction, with an improvement of 2.3% for every addition of weather parameter.

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Meta Learning-Based Hybrid Ensemble Approach for Short-Term Wind Speed Forecasting

Cited by 19 publications

References 41 publications

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Deep Learning for Forecasting-Based Applications in Cyber–Physical Microgrids: Recent Advances and Future Directions

Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

Short-Term Wind Forecasting with Weather Data using Deep Learning - Case Study in Baron Techno Park

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