An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting

Wu, Zhiqiang; Bo, Wang

doi:10.1109/access.2021.3136387

Cited by 21 publications

(11 citation statements)

References 33 publications

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“…While their method improved forecast reliability, it necessitates large datasets for training and imposes high computational demands, potentially limiting its practical feasibility. Wu and Wang [11] presented an Ensemble Neural Network with Improved Algorithms for solar and wind power forecasting. Although their combined approach enhanced forecasting performance, the complexity of integrating various forecasting models may pose challenges for implementation and interpretation operations.…”

Section: In-depth Review Existing Modelsmentioning

confidence: 99%

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

Swapna Choudhary

2024

jes

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The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

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Section: In-depth Review Existing Modelsmentioning

confidence: 99%

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

Swapna Choudhary

2024

jes

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“…Wu and Wang [ 106 ] designed an ensemble neural network ENN framework consisting of LSTM, SVM, ELM, and BP neural networks for solar and wind power prediction in China. The VDM was used to reduce the unwanted effect from the original series.…”

Section: Recent Variants Of Ssamentioning

confidence: 99%

“…Optimizing energy is tackled by SSA and its variants. The following are some examples: power load prediction model [ 90 ], optimal energy management of micro-grid [ 33 ], DMPPT control of photovoltaic microgrid [ 119 ], cost minimization of a hybrid photovoltaic, diesel generator, and battery energy storage system [ 117 ], wind and solar power forecasting [ 106 ], optimal dispatch strategy of microgrid energy storage [ 12 ], harvest energy under uniform and non-uniform irradiance for PV system [ 54 ], and optimization of capacity configuration of wind-solar-diesel-storage [ 85 ].…”

Section: Applications Of Ssamentioning

confidence: 99%

Recent Versions and Applications of Sparrow Search Algorithm

Awadallah

Al-Betar

Doush

et al. 2023

Arch Computat Methods Eng

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This paper reviews the latest versions and applications of sparrow search algorithm (SSA). It is a recent swarm-based algorithm proposed in 2020 rapidly grew due to its simple and optimistic features. SSA is inspired by the sparrow living style of foraging and the anti-predation behavior of sparrows. Since its establishment, it has been utilized for a plethora of optimization problems in different research topics, such as mechanical engineering, electrical engineering, civil engineering, power systems, industrial engineering, image processing, networking, environment, robotics, planing and scheduling, and healthcare. Initially, the growth of SSA and its theoretical features are highlighted in terms of the number of published articles, citations, topics covered, etc. After that, the different extended versions of SSA are reviewed, where the main variations of SSA are produced to avoid premature convergence and to boost the diversity aspects. These extended versions are modifications and hybridization summarized with more focus on the motivations behind establishing these versions. Multi-objective SSA is also presented as another version to deal with Multi-objective optimization problems. The critical analysis of the main research gaps in the convergence behaviour of SSA is discussed. Finally, the conclusion and the possible future expansions are recommended based on the research works accomplished in the literature.

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“…The sparrow search algorithm is a novel metaheuristic algorithm proposed by Xue and Chen (Wu and Wang, 2021), which is based on the imitation of the foraging and anti-predatory behaviors of sparrow populations. The basic idea is to calculate the fitness of individual spar-rows by constructing fitness functions, and then realize the state transformation between individuals, which effectively avoids falling into local optimum.…”

Section: Standard Sparrow Search Algorithmmentioning

confidence: 99%

Short-term load forecasting based on CEEMDAN-FE-ISSA-LightGBM model

Chen²

2023

Front. Energy Res.

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To address the problems of low load forecasting accuracy due to the strong non-stationarity of electric loads, this paper proposes a short-term load forecasting method based on a combination of the complete ensemble empirical modal decomposition adaptive noise method-fuzzy entropy (CEEMDAN-FE) and the Light Gradient Boosting Machine (LightGBM) optimized by the improved sparrow search algorithm (ISSA). First, the original data are decomposed by the complete ensemble empirical modal decomposition adaptive noise algorithm to obtain the eigenmodal components (IMFs) and residual values. Second, the obtained sequences are entropy reorganized by fuzzy entropy, and thus new sequences are obtained. Third, the new sequences are input into the improved sparrow search algorithm-Light Gradient Boosting Machine model for training and prediction. The improved sparrow search algorithm algorithm can realize parameter optimization of the Light Gradient Boosting Machine model to make the data match the model better, and the predicted values of each grouping of the model output are superimposed to obtain the final predicted values. Finally, the effect is compared by the error function, and the comparison results are used to test the performance of the algorithm. The experiments showed that the smallest evaluation metrics were obtained in Case 1 (MAE = 32.251, MAPE = 0.0114,RMSE = 42.386, R2 = 0.997) and Case2 (MAE = 3.866, MAPE = 0.003, RMSE = 5.940, R2 = 0.997).

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An Ensemble Neural Network Based on Variational Mode Decomposition and an Improved Sparrow Search Algorithm for Wind and Solar Power Forecasting

Cited by 21 publications

References 33 publications

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

Recent Versions and Applications of Sparrow Search Algorithm

Short-term load forecasting based on CEEMDAN-FE-ISSA-LightGBM model

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