http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Zhaoyang Qu,Nan Qu,Yaowei Liu,Xiangai Yin,Chong Qu,Wanxin Wang,Jing Han 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.5
With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer’s load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.
Qu, Zhaoyang,Qu, Nan,Liu, Yaowei,Yin, Xiangai,Qu, Chong,Wang, Wanxin,Han, Jing The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.5
With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.
Zhaoyang Qu,Wanxin Wang,Nan Qu,Yuqing Liu,Hongbo Lv,Kewei Hu,Jianyou Yu,Manyang Gao,Jiajun Song 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.4
In order to improve the accuracy of forecasts of the electricity sales of power sales companies, a depth forecast model of electricity sales based on the characteristics of the power market is proposed. First, based on survival analysis, the calculation method of the user churn rate in the electricity market is given, and the number of users at a certain moment in the future is predicted. Then, users’ electricity consumption that calculated by the deep belief network and the predicted quantity of users are combined to design a forecast model of electricity sales. Finally, the model is solved utilizing the weighting algorithm of adaptive inertia. The analysis of the example shows that the proposed method achieves a signifi cant improvement in the accuracy of power sales forecasting.
Probability Prediction Method of Short‑Term Electricity Price Based on Quantile Neural Network Model
Zhaoyang Qu,Manyang Gao,Yuqing Liu,Hongbo Lv,Jian Sun,Miao Li,Wei Liu,Mingshi Cui 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2
Aiming at the inaccuracy of short-term electricity price forecasting in competitive power markets, a probabilistic short-term electricity price forecasting method based on the quantile neural network model is proposed. First, a method for selecting electricity price similarity based on comprehensive infuencing factors is designed to select the forecast data set with similar characteristics to the forecast date. The similar daily quantile regression algorithm is then combined with the generalized dynamic fuzzy neural network to construct a quantile neural network electricity price model for obtaining the predicted daily electricity price condition quantile. Finally, the kernel density function is used to convert the predicted daily electricity price condition quantile into the predicted probability density curve to realize short-term electricity price probability prediction. The data of the electricity market of the city of Dayton, Ohio in the United States is used as an example. The experimental results demonstrate that the proposed method can efectively improve the accuracy of short-term electricity price forecasting
Zhaoyang Qu,Wanxin Wang,Nan Qu,Yuqing Liu,Hongbo Lv,Kewei Hu,Jianyou Yu,Manyang Gao,Jiajun Song 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
Due to unfortunate mistake the grant numbers have been omitted in the acknowledgments section: This work is supported by the National Natural Science Foundation of China (No. 51437003), Jilin Province Science and Technology Development Plan Project of China (20160623004TC, 20180201092GX), Jilin Science and Technology Innovation Development Plan Project of China (201830817).
Zhaoyang Qu,Jiajun Song,Yuqing Liu,Hongbo Lv,Kewei Hu,Jian Sun,Miao Li,Wei Liu,Mingshi Cui,Wanxin Wang 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6
The problem of load fuctuation in the distribution network and increasing power grid cost input caused by the unpredictable behavior of electric vehicle (EV) users in response to electricity price is investigated in this paper. An optimization model method for the charging and discharging price of electric vehicles is proposed, considering the vehicle owner response and power grid cost. The rule of EV user travel is frst analyzed, and the travel and battery state constraints are defned. Under the constraints of user charging and discharging behavior and battery characteristics, a user transfer rate and unit energy cost function is designed to construct a multi-objective model of charging and discharging price that minimizes electricity expenditure and avoids an increase in power grid investment. Finally, an improved multi-target fsh swarm algorithm is presented to solve the model optimization problem. The example analysis shows that the proposed method can reduce the peak-valley load diference of the system and cost input of the power grid, as well as provide users with regulation ability to access the power grid at diferent time periods
A Novel Feature Selection Based Gravitation for Text Categorization
Jieming Yang,Zhiying Liu,Zhaoyang Qu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.3
The high dimensionality of feature space is a big hurdle in applying many sophisticated methods to text categorization. The feature selection method is one of methods which reduce the high dimensionality of feature space. In this paper, we proposed a new feature selection algorithm based on gravitation, named GFS, which regards a feature occurring in one category as an object, and all objects corresponding to a feature occurring in various categories can constitute a gravitational field, then the gravitation of a feature with unknown category label on which all objects in the gravitational field act is used for feature selection. We have evaluated GFS on three benchmark datasets (20-Newgroups, Reuters-21578 and WebKB), using two classification algorithms, Naïve Bayes (NB) and Support Vector Machines (SVM), and compared it with four well-known feature selection algorithms (information gain, document frequency, orthogonal centroid feature selection and Poisson distribution). The experiments show that GFS performs significantly better than other feature selection algorithms in terms of micro F1, macro F1 and accuracy.
Text Representation Based on Key Terms of Document for Text Categorization
Jieming Yang,Zhiying Liu,Zhaoyang Qu 보안공학연구지원센터 2016 International Journal of Database Theory and Appli Vol.9 No.4
The text representation, “bag of words” or vector space model, is widely used by most of the classifiers in text categorization. All the documents fed into the classifier are represented as a vector in the vector space, which consists of all the terms extracted from training set. Due to the characteristics of high dimensionality, feature selection algorithm is usually used to reduce the dimensionality of the vector space. Through feature selection, each document is represented by some representative terms extracted from the training set. Although the classification results based on this document representation methodare better, it is inevitable that some documents may contain few even none representative terms, and these documents must be misclassified. In this paper, we proposed a new text representation method, KT-of-DOC, which represents one document using some key terms extracted from this document. We selected key terms of each document based on six feature selection algorithms, Improved Gini Index (GINI), Information Gain (IG), Mutual Information (MI), Odds Ratio (OR), Ambiguity Measure (AM) and DIA association factor (DIA), respectively, and evaluated the performance of two classifiers, Support Vector Machines (SVM) and K-Nearest Neighbors (KNN), on three benchmark collections, 20-Newsgroups, Reuters-21578 and WebKB. The results show that the proposed representation method can significantly improve the performance of classifier.
A Data-Mining Approach for Wind Turbine Power Generation Performance Monitoring Based on Power Curve
Jianlou Lou,Heng Lu,Jia Xu,Zhaoyang Qu 보안공학연구지원센터 2016 International Journal of Smart Home Vol.10 No.2
A new data-mining approach based on power curve profiles is put forward to monitor the power generation performance of wind turbines in this paper. Through assessing the wind-speed power datasets, the weakened power generation performance of turbines could be identified effectively by this approach. Shapes of power curve profiles over consecutive time intervals are constructed by fitting power curve models into wind-speed power datasets. In this research, we designed the Auto-adapt Optimal Interclass Variance algorithm, optimal constraint in each wind-speed power sub-dataset is explored for governing the data-driven method based on distance-based outlier detection and variance analysis model. The AOIV algorithm achieves the self-optimization of the threshold parameter and reaches a high degree of robustness to variations in wind-power generation performance monitoring. The blind industrial researches are conducted to validate the effectiveness of this approach, also indicates the decrease of error rates while detecting weakened power generation performance and the improvement of turbines’ power output.