Research on power load forecasting of wavelet neural network based on the improved genetic algorithm

Zhang, Ruihong; Yu, Zhichao

doi:10.1080/01430750.2019.1682042

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…On the contrary, approaches that use AI are built on shallow and deep-ordered procedures and can therefore learn complicated and nonlinear structures. Nonetheless, some shallow-based approaches have not worked well for feature mining like Artificial Neural Networks (ANNs), Support Vector Regression [31,32], Random Forest (RF) [33], Wavelet Neural Networks [34], and the Extreme learning Machine model. Therefore, to increase the performance of these shallow-based models, they require many feature selection and feature extraction methods, which present a challenging task.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Alanazi,

Saeed,

Islam

et al. 2023

Sustainability

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Accurate short-term forecasting of electrical energy loads is essential for optimizing energy management in low-carbon buildings. This research presents an innovative two-stage model designed to address the unique challenges of Electricity Load Forecasting (ELF). In the first phase, robust data preprocessing techniques are employed to handle issues such as outliers, missing values, and data normalization, which are common in electricity consumption datasets in the context of low-carbon buildings. This data preprocessing enhances data quality and reliability, laying the foundation for accurate modeling. Subsequently, an advanced data-driven modeling approach is introduced. The model combines a novel residual Convolutional Neural Network (CNN) with a layered Echo State Network (ESN) to capture both spatial and temporal dependencies in the data. This innovative modeling approach improves forecasting accuracy and is tailored to the specific complexities of electrical power systems within low-carbon buildings. The model performance is rigorously evaluated using datasets from low-carbon buildings, including the Individual-Household-Electric-Power-Consumption (IHEPC) dataset from residential houses in Sceaux, Paris, and the Pennsylvania–New Jersey–Maryland (PJM) dataset. Beyond traditional benchmarks, our model undergoes comprehensive testing on data originating from ten diverse regions within the PJM dataset. The results demonstrate a significant reduction in forecasting error compared to existing state-of-the-art models. This research’s primary achievement lies in its ability to offer an efficient and adaptable solution tailored to real-world electrical power systems in low-carbon buildings, thus significantly contributing to the broader framework of modeling, simulation, and analysis within the field.

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Section: Related Workmentioning

confidence: 99%

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Alanazi,

Saeed,

Islam

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Zhang believe that, in the field of primary and secondary schools, taking home school instant messaging as an example, through cooperation with the three operators of communication and schools, establishes an interactive relationship between schools and parents, pays attention to the role of parents in teaching activities, and facilitates parents to understand their children's school situation [9]. Zhang and Yu believe that although the rise of mobile education is expected with the strong participation of giant enterprises such as Netease and Tencent in the online learning industry, researchers still need to explore and practice, which mobile learning application model is more suitable for Chinese Education [10]. For genetic algorithm, Zhang quadratic equation approximation operators are combined with genetic algorithm to solve the function optimization problem [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Mobile Education System Based on Genetic Algorithm

Dang

2022

Mobile Information Systems

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In order to solve the problems existing in the current mobile education system, this paper proposes a method of mobile education system based on genetic algorithm. By analyzing the research status of mobile learning and the theory and application mode of mobile learning, this method explores the combination of mobile learning theory and application mode and China’s traditional primary and secondary education mode, then puts forward a mobile teaching model suitable for primary and secondary education, and builds a mobile teaching system on this basis. The results show that, through the analysis, it is found that most of them remain at about 10%. In practice, the occupation is slightly increased only when multiple people teach, but it also belongs to an acceptable normal level, which can meet more complex functions in the future. The function of mobile learning platform needs to be expanded. For example, mobile learning forum can provide learners with a platform for communication, discussion, and sharing learning materials.

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“…In order to improve the customization experience of users, Meng, Y. and others studied and analyzed men's suits, optimized their customization process, developed a men's suit customization system, and carried out automatic customization design for the suit pattern. The main research object is the suit customization system developed for the user's body shape, and the style of customized clothes also exists in the system, Users only need to choose to customize clothes based on their own body shape, which provides inspiration for the customized automatic design of clothes in this paper [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Research on Style Design of Suit Based on Computer Interactive Genetic Algorithm

Zhang

2022

Mobile Information Systems

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In order to study the suit style design based on computer interactive genetic algorithm. Firstly, this paper summarizes the development status of garment customization and interactive evolutionary computing and studies the steps and system flow of customized garment design based on interactive evolutionary computing and the automatic generation method of garment style. Then, taking the suit as an example, the principal component analysis method is used to extract the elements of clothing design and code it. Combined with the interactive genetic algorithm, the user-satisfied clothing style binary string is generated, and the style binary string is decoded into a visual style map through the decoding algorithm. Finally, based on MATLAB platform, a customized clothing system prototype based on interactive evolutionary computing is realized. Users can carry out innovative clothing design through interactive graphical interface, automatically draw the clothing style designed by users, and provide method and technical support for clothing customization. The experimental results show that in the process of interaction design, try to solve the initial population required by the design, evolve and derive offspring on a large scale through genetic operation, retrieve the whole design feasible region, and obtain the optimal solution in line with user preferences.

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Research on power load forecasting of wavelet neural network based on the improved genetic algorithm

Cited by 6 publications

References 11 publications

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Enhancing Short-Term Electrical Load Forecasting for Sustainable Energy Management in Low-Carbon Buildings

Mobile Education System Based on Genetic Algorithm

Research on Style Design of Suit Based on Computer Interactive Genetic Algorithm

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