SMART GRID ENERGY CONSERVATION MANAGEMENT BASED ON THE UNIVERSITY'S ENERGY INNOVATION HUB OF KNOWLEDGE

BACKGROUND AND OBJECTIVES. Due to increasing energy costs, as well as strict environmental regulations, there is a growing need for greater resource efficiency, which makes energy-efficient solutions necessary. Thus, the importance of innovations based on technologies designed to save energy, such as the Smart Grid, is increasing. Smart Grid is not just a compilation of smart meters or other electrical devices, it is a series of technologies, a concept of a fully integrated, self-regulating and self-healing power grid, which has a network topology and includes all sources of generation, transmission and distribution, managed by a single network of information and control devices and systems.METHODS. As the main method used was the calculation of the synthetic balance of savings from the use of different types of energy resources by the university after the implementation of the application Smart Grid-energy conservation management on the basis of the university energy-innovation Hub of knowledge.FINDINGS. A mechanism for the implementation of the Smart Grid energy-saving management application on the basis of the university energy-innovation Knowledge Hub is proposed. Smart Grid is designed to provide real-time data on the almost instantaneous balance of energy supply and demand. To ensure grid reliability by reducing peak demands and improving energy efficiency, Smart Grid data management is an affordable and effective tool for data analysis and decision making.CONCLUSION. The results of calculation of the predicted effect of the Smart Grid application implementation for the 4th building of Kyiv National University of Technologies and Design proved that the reduction of installed capacity as a result of the project was 80.5%, i.e. a 1% reduction in capacity creates an economic effect of 0.58% of the costs associated with modernization. Given the current level of electricity consumption, we can predict a potential reduction of 951 thousand UAH per year or almost 50% of the cost of electricity consumed in 2020.

Quantitative Flood Damage Evaluation Using Grid-Based Spatial Analysis Data

In Korea, flood damage occurs every year due to typhoons and heavy rains, resulting in increasing damage to human lives and properties in urban areas. To reduce the scale of flood damage, economic analyses of flood-control work are conducted as part of efficient disaster management in the context of a limited budget. In this study, a quantitative evaluation of flood damage in Ulsan due to Typhoon Chaba was conducted using multi-dimensional flood damage analysis (MD-FDA). However, the land cover map applied to MD-FDA has limited data resolution and update intervals. Examination of domestic and foreign research cases to complement these spatial analysis data showed that grid data were being used in disaster-related fields. This study evaluated whether grid data are suitable for quantitative assessment through economic analyses conducted using new spatial analysis data such as road name address digital maps and 100 × 100 m grid-based spatial analysis data. The results of this study confirm that center-point-method grid data constitute spatial analysis data suitable for economic analysis.

Multi-Objective Spatial Suitability Evaluation and Conflict Optimization Considering Productivity, Sustainability, and Livability in Southwestern Mountainous Areas of China

Space is the fundamental carrier for production, living, and ecological activities, and optimizing the spatial pattern is of vital importance to promote regional sustainable development. To achieve this goal, the core issues are to identify the risks of resource and environmental constraints of development and to realize the rational distribution of human living space. Based on the integration of multisource heterogeneous data, taking Yunnan Province, a typical mountainous area in China, as an example, this research proposes a multi-object suitability evaluation method based on 50 × 50 m grid data at the provincial scale. We build a spatial conflict analysis model to identify production–living–ecological space (PLES) and propose governance suggestions for different functional areas. The results show that (1) areas suitable for ecology make up the greatest proportion of Yunnan Province, but areas with living and ecological functions show obvious spatial complementarity; (2) areas suitable for production are restricted by steep slope, geological hazards and fragmented pattern; (3) areas suitable for living is rare, and they are mainly concentrated in the plains of central Yunnan; and (4) twenty-seven percent of area has potential spatial conflicts, among which 4.38% of the area is all suitable for production–living–ecological. The production–living advantage areas are concentrated in the central Yunnan UA (Urban agglomeration), which has a high spatial overlap. These results are expected to provide valuable insights to support comprehensive multifunctional spatial utilization and sustainable development in mountainous areas.

Weighting Methods for Rare Event Identification From Imbalanced Datasets

In machine learning, we often face the situation where the event we are interested in has very few data points buried in a massive amount of data. This is typical in network monitoring, where data are streamed from sensing or measuring units continuously but most data are not for events. With imbalanced datasets, the classifiers tend to be biased in favor of the main class. Rare event detection has received much attention in machine learning, and yet it is still a challenging problem. In this paper, we propose a remedy for the standing problem. Weighting and sampling are two fundamental approaches to address the problem. We focus on the weighting method in this paper. We first propose a boosting-style algorithm to compute class weights, which is proved to have excellent theoretical property. Then we propose an adaptive algorithm, which is suitable for real-time applications. The adaptive nature of the two algorithms allows a controlled tradeoff between true positive rate and false positive rate and avoids excessive weight on the rare class, which leads to poor performance on the main class. Experiments on power grid data and some public datasets show that the proposed algorithms outperform the existing weighting and boosting methods, and that their superiority is more noticeable with noisy data.

Research on Identification of Power Grid Weakness Based on Bayesian Inference

With the gradual reform and development of the power grid, it is of great significance to study how to effectively identify and evaluate the weak links of the power grid for the actual planning, construction, and operation of the power grid. This paper analyzed the power grid’s historical component data and real-time operation state parameters. We established a weak link identification model based on Bayesian reasoning. Firstly, we constructed the node branch Bayesian network according to the network topology relationship. The power transmission distribution factor is modified according to the historical operation load of the grid components, and the conditional probability table is calculated based on the grid structure; finally, we used the maximum possible explanation algorithm in the Bayesian network. The weakness degree of all components in the network is calculated, and the maximum probability weak link sequence is obtained. The correctness and effectiveness of the proposed method are verified by IEEE 39 bus simulation and regional power grid data.

The Triggering Mechanism of Short Video Customer Inspiration – Qualitative Analysis Based on the Repertory Grid Technique

It is believed that stimulating the inspiration of short video consumers might be an effective way to attract and maintain the attention of consumers so that they are willing to respond positively to short video ads. Therefore, in order to explore the source of customer inspiration in short video and its cognitive psychological process, the text and grid data collected from an interview among 25 short video users have been qualitatively analyzed by Kelly Grid Technology in order to construct the formation path model of short video customer inspiration, and find out its source, triggering mechanism, and influencing factors. It is found that the inspiring informational content characteristics include richness, reliability, vividness, and fluency of emotional content characteristics, fun, novelty, and narrative. However, the characteristics of commercial content in short video ads hinder the inspiration of consumers. The study also reveals that an internal mechanism of inspiration stimulation is built on some cognitive processes (i.e., presence, processing fluency, perceived innovation, perceived convenience) generated by informational content, and emotional responses by emotional content (i.e., curiosity, surprise, enjoyment, etc.). In addition, it is shown that personal involvement enhances the relationship between the inspiring content characteristics and consumer inspiration. As a result, customer inspiration and engagement in short video ads are highly enriched. Findings provide implications for short video platforms and online marketers.

Adoption of Blockchain Based Smart Application in Machine Learning

In recent years, the emergence of blockchain technology (BT) has become a novel, most disruptive, and trending technology. The redistributed database in BT emphasizes data security and privacy. Also, the consensus mechanism makes positive that data is secured and bonafide. Still, it raises new security issues like majority attacks and double-spending. To handle the said problems, data analytics is required on blockchain-based secure knowledge. Analytics on these data raises the importance of arising technology Machine Learning (ML). ml involves the rational quantity of data to create precise selections. data reliability and its sharing are terribly crucial in ml to enhance the accuracy of results. the combination of those two technologies (ML and BT) provide give highly precise results. in this paper, present gift a detailed study on ml adoption we BTbased present applications additional resilient against attacks. There area unit varied ancient ML techniques, for example, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms like Convolutional Neural Network (CNN) and Long STM (LSTM) are often used to analyze the attacks on a blockchain-based network. Further, we tend to embody however each the technologies are often applied in many sensible applications like unmanned Aerial Vehicle (UAV), sensible Grid (SG), healthcare, and sensible cities. Then, future analysis problems and challenges are explored. At last, a case study is presented with a conclusion. Keywords: Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

A Novel Multi-Input Multi-Output Recurrent Neural Network Based on Multimodal Fusion and Spatiotemporal Prediction for 0–4 h Precipitation Nowcasting

Multi-source meteorological data can reflect the development process of single meteorological elements from different angles. Making full use of multi-source meteorological data is an effective method to improve the performance of weather nowcasting. For precipitation nowcasting, this paper proposes a novel multi-input multi-output recurrent neural network model based on multimodal fusion and spatiotemporal prediction, named MFSP-Net. It uses precipitation grid data, radar echo data, and reanalysis data as input data and simultaneously realizes 0–4 h precipitation amount nowcasting and precipitation intensity nowcasting. MFSP-Net can perform the spatiotemporal-scale fusion of the three sources of input data while retaining the spatiotemporal information flow of them. The multi-task learning strategy is used to train the network. We conduct experiments on the dataset of Southeast China, and the results show that MFSP-Net comprehensively improves the performance of the nowcasting of precipitation amounts. For precipitation intensity nowcasting, MFSP-Net has obvious advantages in heavy precipitation nowcasting and the middle and late stages of nowcasting.