Multi-Feature Power Load Forecasting Model Based on Hyperparameter Optimization and Ensemble Learning

Power load forecasting is crucial for the economic dispatch and safe operation of power grids, yet the fluctuating and unstable nature of power load data often limits the accuracy of traditional single-model approaches. This paper presents an integrated learning model designed to improve forecast-ing accuracy and stability by addressing these challenges. The model in-corporates improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), a vector-weighted average optimization algorithm (INFO), convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and random forest (RF). By thoroughly analyzing and preprocessing the data, the approach effectively handles its non-linear, non-stationary characteristics. The combination of CNN and BiLSTM enhances the model’s ability to capture temporal and spatial features, while RF strengthens generalization. The INFO algorithm dynamically adjusts weights and parameters during training, resulting in significantly improved predictive performance. Experimental results on data from the Australian electricity market confirm that the proposed model outperforms existing approaches in key performance metrics, showcasing its effectiveness and potential for practical applications in power load forecasting.

Power Transformer Fault Diagnosis Based on Multi Class SVM and IPSO

This article focuses on classifying a variety of faults in power transformers with high precision, using an Improved Particle Swarm Optimization-Support Vector Machine (IPSO-SVM) system designed for fault diagnosis. The process begins with the identification of five distinct gases dissolved in oil, serving as diagnostic features. Minimal output encoding is then used to construct multiple binary support vector machine (SVM), facilitating a multi-class classification of transformer faults. While other studies often combine traditional Particle Swarm Optimization (PSO) algorithms with SVMs, our approach employs an enhanced PSO algorithm. This improved algorithm allows for the optimization of inertia and learning factors, values of which adapt based on iteration counts. The PSO is then leveraged on the optimization of the penalty factor and the radial basis function of SVM, thereby improving its classification performance. Simulation results indicate that our IPSO-SVM methodology achieves 90% and 92% accuracy in training and testing sets, respectively. This method significantly enhances the accurateness oftransformer malfunction diagnosis, exhibiting superior diagnostic precision compared to traditional power transformer malfunction diagnosis methods.

Research on Bearing Fault Diagnosis Methods Based on Various Convolutional Neural Network Architectures

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

24-Step Short-term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions

With the global objectives of achieving a ‘carbon peak’ and ‘carbon neutrality’, along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from January 1st to December 30th in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Study on Preparation Technology of Novel Fluorosilicone Rubber Composite Insulator

Abstract Currently, commonly used external insulation materials for power products lack operational stability compared to high-temperature vulcanized silicone rubber. The excellent hydrophobicity of PTFE provides a direction for further enhancing high-temperature vulcanized silicone rubber. By utilizing fluorinated occupation directional design technology and improving the dispersion degree of the dispersed phase, we have successfully prepared high-temperature vulcanized fluorine silicone rubber. After testing its flammability and other aspects, the prepared material achieved HC1-level hydrophobicity, surpassing conventional silicone rubber insulators in terms of self-cleaning performance.

Influences on police self-efficacy: public service motivation, job burnout, and organizational support

A 24-Step Short-Term Power Load Forecasting Model Utilizing KOA-BiTCN-BiGRU-Attentions

With the global objectives of achieving a “carbon peak” and “carbon neutrality” along with the implementation of carbon reduction policies, China’s industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications.

Influencing on police-community relations: an empirical study based on the OIC three-dimensional model

AbstractThe establishment of harmonious police-community relations is a important factor in advancing the modernization of public security endeavors, linked to the exercise of authority, value orientations, and the core mission of law enforcement agencies. The OIC model considers police professional prestige, image, and credibility. Utilizing the OIC three-dimensional model as a foundation, this study examines the intricate mechanisms and explanatory frameworks underlying the relationship between police professional prestige, police image, police law enforcement credibility, and police-community relations. This study surveyed police officers and citizens in X city. Through a comprehensive questionnaire survey and rigorous data analysis, the study reveals several noteworthy findings: (1) police professional prestige has a significant positive predictive effect on police-community relations; (2) police image plays a partially mediating role between police professional prestige and police-community relations; (3) police law enforcement credibility plays a partially mediating role between police professional prestige and police-community relations; (4) police image and police law enforcement credibility can play a chain mediating role between police professional prestige and police-community relations; (5)police image and police law enforcement credibility jointly play a sequential mediating role, bridging the gap between police professional prestige and police-community relations. Based on the OIC three-dimensional model, the study proposes feasible countermeasures and theoretical references for closer police-community relations from the aspects of comprehensive cognition, objective reality and mediated communication.

Evaluation and Analysis Model of Light Pollution Risk Level Based on Multiple Indicators

A new environmental problem-light pollution has gradually attracted the attention of governments all over the world. To develop a model to evaluate the risk level of light pollution， we have set up three first-level indicators which are regional development level, population, and biological environment, and set up two-level indicators under them like: urbanization rate, lighting intensity, biology diversity.etc.We use the light pollution index obtained by satellite detection, and get the final regression equation of light pollution index by ridge regression fitting with the data of 28 provinces, and then verify it with the image observed by Luojia –1, and determine the model equation of Light Pollution Index. Then we optimize the model through both geography and climate, and correct the provinces where the light pollution index fitted by the regression equation is different from the real light pollution index, making the model accuracy further improved.

Research on the Influence of Heavy Pollution Environment on Hydrophobic Substances in Silicone Rubber Materials

Power load forecasting is very important for power dispatching. Accurate load forecasting is of great significance for saving energy, reducing generating costs, and improving social and economic benefits. In order to accurately predict the power load, based on BP neural network theory, combined with the advantages of Clementine in dealing with big data and preventing overfitting, a neural network prediction model for large data is constructed.

Research on infant behavior feature classification based on GWO-ELM algorithm

Research on the Strategy of Hosting the Olympic Games Based on Entropy-Weighted Gray Correlation Analysis

Regarding the issue of Olympic Games hosting strategy, first, the entropy weight method and grey correlation analysis are used to construct the overall model. Secondly, the CGE model is used to comprehensively analyze the impacts of the two Olympic policies on production, national income, and national trade by considering land, income, and trade. Thirdly, the Transformer algorithm is used to make predictions to avoid the limitations of the CGE model. Finally, the predicted growth rates of Olympic tourism and GDP for the next year are used to comprehensively evaluate the two strategies of hosting the Olympics by season and by a fixed location. The study concludes that, based on the comprehensive impact of land, income, trade, and tourism, the weights for these factors are 0.26, 0.24, 0.24, and 0.26, respectively. Meanwhile, the comprehensive scores for hosting the Olympics by season and by a fixed location are 1.3569 and 1.2414, respectively.