EBD Methodology in the Education in the Mid-west University of China

Due to the impact of globalization,more and more universities in China dedicate to improve the quality of undergraduate education.The particular emphasis was given on the universities in Mid-west university of China,where the economics and transportation facility are relatively less developed comparing to other regions.In contrast of the traditional methodologies of social science,this research was conducted using an engineering design methodology,called Environment-Based Design(EBD).The EBD procedure allows to derive the solutions step-by-step based on a holistic environment analysis and thus an identification of major challenges.

A novel non-intrusive load monitoring technique using semi-supervised deep learning framework for smart grid

Non-intrusive load monitoring(NILM)is a technique which extracts individual appliance consumption and operation state change information from the aggregate power consumption made by a single residential or commercial unit.NILM plays a pivotal role in modernizing building energy management by disaggregating total energy consumption into individual appliance-level insights.This enables informed decision-making,energy optimization,and cost reduction.However,NILM encounters substantial challenges like signal noise,data availability,and data privacy concerns,necessitating advanced algorithms and robust methodologies to ensure accurate and secure energy disaggregation in real-world scenarios.Deep learning techniques have recently shown some promising results in NILM research,but training these neural networks requires significant labeled data.Obtaining initial sets of labeled data for the research by installing smart meters at the end of consumers’appliances is laborious and expensive and exposes users to severe privacy risks.It is also important to mention that most NILM research uses empirical observations instead of proper mathematical approaches to obtain the threshold value for determining appliance operation states(On/Off)from their respective energy consumption value.This paper proposes a novel semi-supervised multilabel deep learning technique based on temporal convolutional network(TCN)and long short-term memory(LSTM)for classifying appliance operation states from labeled and unlabeled data.The two thresholding techniques,namely Middle-Point Thresholding and Variance-Sensitive Thresholding,which are needed to derive the threshold values for determining appliance operation states,are also compared thoroughly.The superiority of the proposed model,along with finding the appliance states through the Middle-Point Thresholding method,is demonstrated through 15%improved overall improved F1micro score and almost 26%improved Hamming loss,F1 and Specificity score for the performance of individual appliance when compared to the benchmarking techniques that also used semi-supervised learning approach.

Integrated Equipment Health Prognosis Considering Crack Initiation Time Uncertainty and Random Shock

With integrated equipment health prognosis, both physical models and condition monitoring data are utilized to achieve more accurate prediction of equipment remaining useful life （RUL）. In this paper, an integrated prognostics method is proposed to account for two important factors which were not considered before, the uncertainty in crack initiation time （CIT） and the shock in the degradation. Prognostics tools are used for RUL pre- diction starting from the CIT. However, there is uncertainty in CIT due to the limited capability of existing fault detection tools, and such uncertainty has not been explic- itly considered in the literature for integrated prognosis. A shock causes a sudden damage increase and creates a jump in the degradation path, which shortens the total lifetime, and it has not been considered before in the integrated prognostics framework either. In the proposed integrated prognostics method, CIT is considered as an uncertain parameter, which is updated using condition monitoring data. To deal with the sudden damage increase and reduction of total lifetime, a virtual gradual degradation path with an earlier CIT is introduced in the proposed method. In this way, the effect of shock is captured through identifying an appropriate CIT. Examples of gear prog- nostics are given to demonstrate the effectiveness of the proposed method.

A Hybrid Model for Short-term PV Output Forecasting Based on PCA-GWO-GRNN

High-precision day-ahead short-term photovoltaic(PV)output forecasting is essential in PV integration to the smart distribution networks and multi-energy system,and provides the foundation for the security,stability,and economic operation of PV systems.This paper proposes a hybrid model based on principal component analysis,grey wolf optimization and generalized regression neural network(PCA-GWO-GRNN)for day-ahead short-term PV output forecasting,considering the features of multiple influencing factors and strong uncertainty.This paper first uses the PCA to reduce the dimension of meteorological features.Then,the high-precision day-ahead short-term PV output forecasting based on GWO-GRNN model is realized.GRNN is used to regressively analyze the input features after dimension reduction,and the parameter of GRNN is optimized by using GWO,which has strong global searching ability and fast convergence.The proposed PCA-GWO-GRNN model effectively achieves a high precision in day-ahead shortterm PV output forecasting,which is demonstrated in a case study on a real PV plant in Jiangsu province,China.The results have validated the accuracy and applicability of the proposed model in real scenarios.

Evaluation of the situational awareness effects for smart distribution networks under the novel design of indicator framework and hybrid weighting method

As a key application of smart grid technologies,the smart distribution network(SDN)is expected to have a high diversity of equipment and complexity of operation patterns.Situational awareness(SA),which aims to provide a critical visibility of the SDN,will enable a significant assurance for stable SDN operations.However,the lack of systematic evaluation through the three stages of perception,comprehensive,and prediction may prevent the SA technique from effectively achieving the performance necessary to monitor and respond to events in SDN.To analyze the feasibility and effectiveness of the SA technique for the SDN,a comprehensive evaluation framework with specific performance indicators and systematic weighting methods is proposed in this paper.Besides,to implement the indicator framework while addressing the key issues of human expert scoring ambiguity and the lack of data in specific SDN areas,an improved interval-based analytic hierarchy process-based subjective weighting and a multi-objective programming method-based objective weighting are developed to evaluate the SDN SA performance.In addition,a case study in a real distribution network of Tianjin,China is conducted whose outcomes verify the practicality and effectiveness of the proposed SA technique for SDN operating security.

Multivariate Two-stage Adaptive-stacking Prediction of Regional Integrated Energy System

To reduce environmental pollution and improve the efficiency of cascaded energy utilization, regional integrated energy system(RIES) has received extensive attention. An accurate multi-energy load prediction is significant for RIES as it enables stakeholders to make effective decisions for carbon peaking and carbon neutrality goals. To this end, this paper proposes a multivariate two-stage adaptive-stacking prediction(M2ASP) framework. First, a preprocessing module based on ensemble learning is proposed. The input data are preprocessed to provide a reliable database for M2ASP, and highly correlated input variables of multi-energy load prediction are determined. Then, the load prediction results of four predictors are adaptively combined in the first stage of M2ASP to enhance generalization ability. Predictor hyper-parameters and intermediate data sets of M2ASP are trained with a metaheuristic method named collaborative atomic chaotic search(CACS) to achieve the adaptive staking of M2ASP. Finally, a prediction correction of the peak load consumption period is conducted in the second stage of M2ASP. The case studies indicate that the proposed framework has higher prediction accuracy, generalization ability, and stability than other benchmark prediction models.

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.