A Multi-Level Investment Demand Assessment Model for Renewable-Dominated Power System Planning

To achieve the goal of carbon peak and carbon neutrality, the integration of diversified renewable energy will be the principal feature of the planning framework of the smart grid, and the planning direction and focus of power systems would shift to the network transmittability and flexibility enhancement. This paper presents an infrastructure investment demand assessment model based on multi-level analysis method for the renewable-dominated power system planning. First, for the load side, the composite capacity ratio is used to assess the capacity demand of power transformation infrastructure for satisfying the load growth. Then, the renewable energy permeability is adopted as the basis to assess the extensional transmittability capacity for the integration of high renewables. Furthermore, the capacity demand of flexible transmission lines for power grid flexibility enhancement is also estimated. Finally, the amount of unit investment for source-network-load infrastructure capacities can be predicted based on the least square generation adjunctive network and support vector machine (LSGAN-SVM) algorithm. The performance of the proposed model has been tested and benchmarked on a practical-sized power system to verify its effectiveness and feasibility.

Evaluation of Street in Luohu District of Shenzhen on Semantic of Street Image

Based on the theoretical extension of the greening vision and the application practice of streetscape big data, the average green vision rate within the planned green area coverage block of Luohu District, Shenzhen is calculated by PHOTOSHOP and FCN software, and the differences in spatial distribution and current status characteristics between its 3D green vision rate and the management unit control guidance map of Shenzhen Green Space System Planning (2014-2030) are explored, and the results show that the green space rate in the main urban area of Luohu District, Shenzhen is 36.78%, which is much better than the average level of major cities in the world, but there is still a gap compared with the management unit control guidance map of Shenzhen Green Space System Planning (2014-2030), and this paper proposes optimization suggestions for the current deficiency.

Comparison of Regression and Neural Network Model for Short Term Load Forecasting: A Case Study

Short term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors such as time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations used for prediction, later the model improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR observed as 47% and it reduced to 8.9% for CFBP.

Using Machine Learning to support health system planning during the Covid-19 pandemic: a case study using data from São José dos Campos (Brazil)

Many efforts were made by the scientific community during the Covid-19 pandemic to understand the disease and better manage health systems' resources. Believing that city and population characteristics influence how the disease spreads and develops, we used Machine Learning techniques to provide insights to support decision-making in the city of São José dos Campos (SP), Brazil. Using a database with information from people who undergo the Covid-19 test in this city, we generate and evaluate predictive models related to severity, need for hospitalization and period of hospitalization. Additionally, we used the SHAP value for models' interpretation of the most decisive attributes influencing the predictions. We can conclude that patient age linked to symptoms such as saturation and respiratory distress and comorbidities such as cardiovascular disease and diabetes are the most important factors to consider when one wants to predict severity and need for hospitalization in this city. We also stress the need of a greater attention to the proper collection of this information from citizens who undergo the Covid-19 diagnosis test.