Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?

BACKGROUND Efficiently detecting Parkinson's disease(PD)with dementia(PDD)as soon as possible is an important issue in geriatric medicine.AIM To develop a model for predicting PDD based on various neuropsychological tests using data from a nationwide survey conducted by the Korean Centers for Disease Control and Prevention and to present baseline data for the early detection of PDD.METHODS This study comprised 289 patients who were 60 years or older with PD[110 with PDD and 179 Parkinson's Disease-Mild Cognitive Impairment(PD-MCI)].Regression with optimal scaling(ROS)was used to identify independent relationships between the neuropsychological test results and PDD.RESULTS In the ROS analysis,Korean version of mini mental state ex-amination(MMSE)(KOREAN version of MMSE)(b=-0.52,SE=0.16)and Hoehn and Yahr staging(b=0.44,SE=0.19)were significantly effective models for distinguishing PDD from PD-MCI(P<0.05),even after adjusting for all of the Parkinson's motor symptom and neuropsychological test results.The optimal number of categories(scaling factors)for KOREAN version of MMSE and Hoehn and Yahr Scale was 10 and 7,respectively.CONCLUSION The results of this study suggest that among the various neuropsychological tests conducted,the optimal classification scores for KOREAN version of MMSE and Hoehn and Yahr Scale could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.

Analysis on Irrational Decision Making of Tobacco Family Farmers from the Perspective of Optimal Scale of Efficiency

The larger the difference between the willingness scale of tobacco family farmers and the optimal scale of efficiency,the greater the degree of irrationality,and the higher the decision making risk.With the aid of DEA model,this study calculated the optimal scale of efficiency of Guiyang tobacco family farms.Using the ratio of willingness scale and efficiency optimal scale,it measured the degree of irrationality of family farmers.In addition,with the help of multiple linear regression model,it explained the irrational decision making mechanism of family farmers.Finally,it made a portrait of farmers who tend to make irrational decisions,to find specific farmers and guide them in their production and operation,reduce the risk of planting scale decision making and stabilize the sustainable development of the tobacco industry.

A SUPERLINEARLY CONVERGENT SPLITTING FEASIBLE SEQUENTIAL QUADRATIC OPTIMIZATION METHOD FOR TWO-BLOCK LARGE-SCALE SMOOTH OPTIMIZATION

This paper discusses the two-block large-scale nonconvex optimization problem with general linear constraints.Based on the ideas of splitting and sequential quadratic optimization(SQO),a new feasible descent method for the discussed problem is proposed.First,we consider the problem of quadratic optimal(QO)approximation associated with the current feasible iteration point,and we split the QO into two small-scale QOs which can be solved in parallel.Second,a feasible descent direction for the problem is obtained and a new SQO-type method is proposed,namely,splitting feasible SQO(SF-SQO)method.Moreover,under suitable conditions,we analyse the global convergence,strong convergence and rate of superlinear convergence of the SF-SQO method.Finally,preliminary numerical experiments regarding the economic dispatch of a power system are carried out,and these show that the SF-SQO method is promising.

Novel Block Chain Technique for Data Privacy and Access Anonymity in Smart Healthcare

The Internet of Things (IoT) and Cloud computing are gaining popularity due to their numerous advantages, including the efficient utilization of internetand computing resources. In recent years, many more IoT applications have beenextensively used. For instance, Healthcare applications execute computations utilizing the user’s private data stored on cloud servers. However, the main obstaclesfaced by the extensive acceptance and usage of these emerging technologies aresecurity and privacy. Moreover, many healthcare data management system applications have emerged, offering solutions for distinct circumstances. But still, theexisting system has issues with specific security issues, privacy-preserving rate,information loss, etc. Hence, the overall system performance is reduced significantly. A unique blockchain-based technique is proposed to improve anonymityin terms of data access and data privacy to overcome the above-mentioned issues.Initially, the registration phase is done for the device and the user. After that, theGeo-Location and IP Address values collected during registration are convertedinto Hash values using Adler 32 hashing algorithm, and the private and publickeys are generated using the key generation centre. Then the authentication is performed through login. The user then submits a request to the blockchain server,which redirects the request to the associated IoT device in order to obtain thesensed IoT data. The detected data is anonymized in the device and stored inthe cloud server using the Linear Scaling based Rider Optimization algorithmwith integrated KL Anonymity (LSR-KLA) approach. After that, the Time-stamp-based Public and Private Key Schnorr Signature (TSPP-SS) mechanismis used to permit the authorized user to access the data, and the blockchain servertracks the entire transaction. The experimental findings showed that the proposedLSR-KLA and TSPP-SS technique provides better performance in terms of higherprivacy-preserving rate, lower information loss, execution time, and Central Processing Unit (CPU) usage than the existing techniques. Thus, the proposed method allows for better data privacy in the smart healthcare network.

A New Pitch Detection Algorithm Based on Wavelet Transform

In this paper, a new event detection pitch detector based on the dyadic wavelet transform was constrcted by selecting an optimal scale. The proposed pitch detector is accurate, robust to noise and computationally simple. Experiments show the superior performance of this event-based pitch detector in comparison with previous event-based pitch detector and classical pitch detectors that use the autocorrelation and the cepsmun methods to estimate the pitch period.

Model selection for SVM using mutative scale chaos optimization algorithm

This paper proposes a new search strategy using mutative scale chaos optimization algorithm （MSCO） for model selection of support vector machine （SVM）. It searches the parameter space of SVM with a very high efficiency and finds the optimum parameter setting for a practical classification problem with very low time cost. To demonstrate the performance of the proposed method it is applied to model selection of SVM in ultrasonic flaw classification and compared with grid search for model selection. Experimental results show that MSCO is a very powerful tool for model selection of SVM, and outperforms grid search in search speed and precision in ultrasonic flaw classification.

Simplified Group Search Optimizer Algorithm for Large Scale Global Optimization

A simplified group search optimizer algorithm denoted as"SGSO"for large scale global optimization is presented in this paper to obtain a simple algorithm with superior performance on high-dimensional problems.The SGSO adopts an improved sharing strategy which shares information of not only the best member but also the other good members,and uses a simpler search method instead of searching by the head angle.Furthermore,the SGSO increases the percentage of scroungers to accelerate convergence speed.Compared with genetic algorithm(GA),particle swarm optimizer(PSO)and group search optimizer(GSO),SGSO is tested on seven benchmark functions with dimensions 30,100,500 and 1 000.It can be concluded that the SGSO has a remarkably superior performance to GA,PSO and GSO for large scale global optimization.

SCALE OPTIMIZATION FOR DISTORTED MODEL ON DIVERSION SYSTEM OF HYDROPOWER STATION

Long diversion system of hydropower station, including surge tank, inlet tunnel and penstock, is an inseparable whole system. Scale effects of distorted model on the diversion system are studied in the present paper. Based on the concept of model scale correlation and the method of parameter expression of model scale, the model scale optimization can be realized. Furthermore, a quantitative criterion for the choice of distorted model or normal model is presented. The study shows that distorted model can simultaneously satisfy the similarity conditions derived from surge wave equations, water hammer equations and wave speed equation for the diversion system. In addition, an example for the design of a practical distorted model is provided.

SEQUENTIAL CONVEX PROGRAMMING METHODS FOR SOLVING LARGE TOPOLOGY OPTIMIZATION PROBLEMS： IMPLEMENTATION AND COMPUTATIONAL RESULTS

In this paper, we describe a method to solve large-scale structural optimization problems by sequential convex programming （SCP）. A predictor-corrector interior point method is applied to solve the strictly convex subproblems. The SCP algorithm and the topology optimization approach are introduced. Especially, different strategies to solve certain linear systems of equations are analyzed. Numerical results are presented to show the efficiency of the proposed method for solving topology optimization problems and to compare different variants.

DATA PREORDERING IN GENERALIZED PAV ALGORITHM FOR MONOTONIC REGRESSION

Monotonic regression （MR） is a least distance problem with monotonicity constraints induced by a partiaily ordered data set of observations. In our recent publication [In Ser. Nonconvex Optimization and Its Applications, Springer-Verlag, （2006） 83, pp. 25-33], the Pool-Adjazent-Violators algorithm （PAV） was generalized from completely to partially ordered data sets （posets）. The new algorithm, called CPAV, is characterized by the very low computational complexity, which is of second order in the number of observations. It treats the observations in a consecutive order, and it can follow any arbitrarily chosen topological order of the poset of observations. The CPAV algorithm produces a sufficiently accurate solution to the MR problem, but the accuracy depends on the chosen topological order. Here we prove that there exists a topological order for which the resulted CPAV solution is optimal. Furthermore, we present results of extensive numerical experiments, from which we draw conclusions about the most and the least preferable topological orders.