Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

A Novel Perspective towards SVM Combined with Autoencoder

In this paper, we propose a novel perspective towards the hybrid algorithm about support vector machine combined with neural network. We suggest that the depth of convolution neural network is supposed to insight the view of machines to acquiring an equal level of features as human do. The kernel function of support vector machine can be grasped flexibly where the neural network makes an efficient cross calculation for features exactly instead of the kernel function but more adjustable. To develop such a coincident format, we build a hybrid model with the half former part of autoencoder working as the kernel function and support vector machine working as the core classifier, with certain ways to train the hybrid model: discrete, continuous and prejudice. The hybrid model inherits asset of each algorithm, and that process is generally subject to the objective perspective. We take the hybrid model to Covid 19 detection compared with other well-performed models, and experimental results illustrate that our perspective is advisable which achieves a state-of-the-art performance in medical scheme.

House Price Valuation Model Based on Geographically Neural Network Weighted Regression: The Case Study of Shenzhen, China

Confronted with the spatial heterogeneity of the real estate market, some traditional research has utilized geographically weighted regression (GWR) to estimate house prices. However, its predictive power still has some room to improve, and its kernel function is limited in some simple forms. Therefore, we propose a novel house price valuation model, which is combined with geographically neural network weighted regression (GNNWR) to improve the accuracy of real estate appraisal with the help of neural networks. Based on the Shenzhen house price dataset, this work conspicuously captures the variable spatial regression relationships at different regions of different variables, which GWR has difficulty realizing. Moreover, we focus on the performance of GNNWR, verify its robustness and superiority, and refine the experiment process with 10-fold cross-validation. In contrast with the ordinary least squares (OLS) model, our model achieves an improvement of about 50% on most of the metrics. Compared with the best GWR model, our thorough experiments reveal that our model improves the mean absolute error (MAE) by 13.5% and attains a decrease of the mean absolute percentage error (MAPE) by 13.0% in the evaluation on the validation dataset. It is a practical and powerful way to assess house prices, and we believe our model could be applied to other valuation problems concerning geographical data to promote the prediction accuracy of socioeconomic phenomena.

Numerical Analysis and Scientific Calculation Considering the Management Mechanism of College Students’ Innovation and Entrepreneurship Education

Purpose. This article aims to study how to analyze and study the numerical value of education management mechanisms based on cloud computing and describe the innovation and entrepreneurship of college students. Methodology. This article addresses the problems of numerical analysis and scientific computing. This problem is based on cloud computing, so it elaborates on the concepts and related algorithms of cloud computing and big data and designs and analyzes cases of numerical analysis and scientific computing of educational management mechanisms. Research Findings. Through the research of different kernel functions, the IG_CDmRMR algorithm can obtain relatively high accuracy results for numerical analysis and scientific computing. The IG_CDmRMR algorithm is the closest to expert evaluation. The maximum difference is 0.002, which is consistent in sample three. The maximum difference of the IG algorithm is 0.005, and the minimum difference is 0.002. The evaluation effect of the IG_CDmRMR algorithm is closer to the evaluation effect of experts. Practical Implications. It analyzes the numerical value of the education management mechanism and finds that the accuracy has a certain height. This has certain evaluation significance for the management mechanism of college students’ innovation and entrepreneurship education.

Developing a machine learning framework to determine the spread of COVID-19 in the USA using meteorological, social, and demographic factors

Coronavirus disease of 2019 (COVID-19) has become a pandemic in the matter of a few months, since the outbreak in December 2019 in Wuhan, China. We study the impact of weather factors including temperature and pollution on the spread of COVID-19. We also include social and demographic variables such as per capita gross domestic product (GDP) and population density. Adapting the theory from the field of epidemiology, we develop a framework to build analytical models to predict the spread of COVID-19. In the proposed framework, we employ machine learning methods including linear regression, linear kernel support vector machine (SVM), radial kernel SVM, polynomial kernel SVM, and decision tree. Given the nonlinear nature of the problem, the radial kernel SVM performs the best and explains 95% more variation than the existing methods. In line with the literature, our study indicates the population density is the critical factor to determine the spread. The univariate analysis shows that a higher temperature, air pollution, and population density can increase the spread. On the other hand, a higher per capita GDP can decrease the spread.

SVM-kNN- IPSO ensemble method for Diagnosis of Novel Coronavirus (COVID-19) with CT images

New coronavirus epidemic- COVID- 19 is still growing. This epidemic disease not only includes high mortality due to viral infection but also caused the psychological disaster in all parts of the world. The paper provides the early Coronavirus stage detection COVID-19, with the methods of machine learning. Support vector machine (SVM) is a two-class classifier which in the recent years attracted a significant attention. The performance of this classifier depends on the amount of its parameters such as C (Penalty Factor) and the existing parameter in kernel. Also the selection of a suitable kernel function has a significant affect in its performance improvement. Besides the mentioned cases, performing the feature selection process not only causes to improve the mentioned performance improvement but also causes to reduce the computation complexity and training time. In this paper, we used the improved partial swarm optimization algorithm (IPSO) to optimize the SVM. Findings illustrated that proposed method could be utilized for diagnosing disease of COVID-19 as the assistant system. Promisingly, the proposed method can be regarded as a useful clinical decision tool for the physicians._