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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 Cuan,Da-Wei Ding,Heng Wang,Yahui Liu,Yulong Liu 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.4
This paper develops a novel control method for solving the challenging problem of simultaneous collision avoidance and target tracking for an unmanned ground vehicle with velocity and heading rate constraints. The problem is formulated as an optimization problem where the trajectory tracking is treated as a soft constraint via control Lyapunov functions and obstacles avoidance is treated as hard constraint via control barrier functions. Two constraints are naturally unified in the context of quadratic programs which ensure that the hard constraint, soft constraint, velocity and heading rate constraints are satisfied simultaneously. Under the proposed strategy, an unmanned ground vehicle can avoid the stationary and moving obstacles while tracking the desired trajectory precisely and smoothly. Finally, three representative simulations are provided to demonstrate the effectiveness of the proposed method.
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
( Qiuke Hou ),( Yongquan Huang ),( Yan Wang ),( Liu Liao ),( Zhaoyang Zhu ),( Wenjie Zhang ),( Yongshang Liu ),( Peiwu Li ),( Xinlin Chen ),( Fengbin Liu ) 한국미생물 · 생명공학회 2020 Journal of microbiology and biotechnology Vol.30 No.10
Our previous report determined that miR-144 is a key regulator of intestinal epithelial permeability in irritable bowel syndrome with diarrhea (IBS-D) rats. Recent evidence has shown that lactobacilli play an important role in the relief of IBS-D symptoms. However, few studies have addressed the mechanisms by which microRNAs and lactobacilli exert their beneficial effects on intestinal epithelial permeability. Hence, to elucidate whether miRNAs and lactobacilli play roles in intestinal epithelial barrier regulation, we compared miRNA expression levels in intestinal epithelial cells (IECs) under Lactobacillus casei (L. casei LC01) treatment. IECs and L. casei LC01 were co-cultured and then subjected to microRNA microarray assay. qRT-PCR, western blot and ELISA were used to detect the expression of occludin (OCLN) and zonula occludens 1 (ZO1/TJP1). The interaction between miRNAs and L. casei LC01 acting in IECs was investigated through transfection of RNA oligoribonucleotides and pcDNA 3.1 plasmid. The results are as follows: 1) L. casei LC01 decreased the expression of miR-144 and FD4 and promoted OCLN and ZO1 expression in IECs; 2) L. casei LC01 enhanced the barrier function of IECs via downregulation of miR-144 and upregulation of OCLN and ZO1; 3) Under L. casei LC01 treatment, OCLN and ZO1 overexpression could partially eliminate the promoting effect of miR-144 on intestinal permeability in IECs. Our results demonstrate that L. casei LC01 regulates intestinal permeability of IECs through miR-144 targeting of OCLN and ZO1. L. casei LC01 can be a possible therapeutic target for managing dysfunction of the intestinal epithelial barrier.
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.
A Cognition-inspired System for Data Stream Clustering
Zhaoyang Sun,K. Z. Mao,Wenyin Tang,Lee-Onn Mak,Kuitong Xian,Ying Liu 보안공학연구지원센터 2015 International Journal of u- and e- Service, Scienc Vol.8 No.8
In applications such as target detection, domain knowledge of sensed data is often available. In this paper, we incorporate the available domain knowledge into clustering process and develop a knowledge-driven Mahalanobis distance-based ART (adaptive resonance theory) clustering algorithm. The strength of the knowledge-driven algorithm is that it can automatically determine the number of clusters with improved clustering results. The validity of the new algorithm has been verified on four artificial datasets. In addition, the algorithm has been adopted in our cognition-inspired system for clustering data stream, where known target library and dispersion of feature or attributes are available. The basic idea of this system is to divide data stream into frames, and to incorporate knowledge learned in previous frames into clustering of the following ones. Experimental studies have demonstrated that the evolving learning mechanism leads to improved clustering results compared with conventional incremental clustering algorithm Fuzzy ART and batch-based clustering algorithm k-means.
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,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.
Wei Liu,Wanli Cui,Mingji Chen,Qunfang Hu,Zhaoyang Song 대한토목학회 2023 KSCE Journal of Civil Engineering Vol.27 No.5
This study proposes the risk assessment framework of water distribution networks with a focus on the determination of weights of various risk indices. First, the pipe risk list is built, in which pipe failure probability and consequence are considered. By integrating the analytic hierarchy process (AHP) and the entropy weighting method (EWM), the weights of these indices are obtained by the combined weighting method (CWM), by which both human experience and data distribution can be considered comprehensively. Taking a real WDN in an industrial zone in China as an example, the proposed method is demonstrated in detail and the risks of all pipes are determined. The accuracy of the CWM is validated from the perspective of risk levels of pipes with historical failure records. It is found that the AHP or EWM underestimates or overestimates pipe risks, respectively. Compared to them, the CWM provides reasonable results. In addition, the risks of plastic pipes, newly buried pipes less than five years old, and pipes over twenty years old are much higher.
Wei Liu,Zhaoyang Song,Huiquan Miao 대한토목학회 2018 KSCE JOURNAL OF CIVIL ENGINEERING Vol.22 No.3
Buried pipes are important components of water and gas supply systems. A series of investigations indicate that buried pipes in China suffered serious damages in previous strong earthquakes. Therefore, seismic response analysis and design of buried pipes is an important research topic. In this study, a program is proposed to give the modified factors for segmented pipes in Chinese pipe seismic design code based on the probability Density Evolution Method (PDEM). A stochastic analysis method based on a finite element model of buried pipes and the Probability Density Evolution Method (PDEM) is proposed to determine probability density functions of seismic responses of buried segmented pipes. Then, seismic responses with different guaranteed rates can be derived and adopted for the design of buried pipes in preparation for earthquakes. Stochastic seismic responses of three segmented pipes buried in four sites are calculated using the proposed method. Joint deformations with guaranteed rates of 50%, 80%, 90%, 95%, and 99% are then summarized, and the modified factors for segmented pipes are derived to modify the Chinese code.