scholarly journals PIDS: An Intelligent Electric Power Management Platform

2020 ◽  
Vol 34 (08) ◽  
pp. 13220-13227
Author(s):  
Yongqing Zheng ◽  
Han Yu ◽  
Yuliang Shi ◽  
Kun Zhang ◽  
Shuai Zhen ◽  
...  
Keyword(s):  
Power Consumption ◽  
Stable Operation ◽  
Economic Activities ◽  
Power Outage ◽  
Fine Grained ◽  
Management Platform ◽  
Power Adjustment ◽  
Intelligent Decision ◽  
Information Tracking ◽  
Intelligent Decision Support

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

scholarly journals Optimizing Smart Grid Operations from the Demand Side

AI Magazine ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 28-37
Author(s):  
Yongqing Zheng ◽  
Han Yu ◽  
Yuliang Shi ◽  
Kun Zhang ◽  
Shuai Zhen ◽  
...  
Keyword(s):  
Power Grid ◽  
Stable Operation ◽  
Demand Side ◽  
Power Outage ◽  
Fine Grained ◽  
Management Platform ◽  
Intelligent Decision ◽  
Main Components ◽  
Planning And Construction ◽  
Intelligent Decision Support

As demand for electricity grows in China, the existing power grid is coming under increasing pressure. Expansion of power generation and delivery capacities across the country requires years of planning and construction. In the meantime, to ensure safe operation of the power grid, it is important to coordinate and optimize the demand side usage. In this paper, we report on our experience deploying an artificial intelligence (AI)–empowered demand-side management platform – the Power Intelligent Decision Support (PIDS) platform – in Shandong Province, China. It consists of three main components: 1) short-term power consumption gap prediction, 2) fine-grained Demand Response (DR) with optimal power adjustment planning, and 3) Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. PIDS has been deployed since August 2018. It is helping over 400 companies optimize their power usage through DR, while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS due to forced shutdown has been reduced from 16% to 0.56%.

scholarly journals Intelligent Decision Support for Improving Power Management

2019 ◽  
Author(s):  
Yongqing Zheng ◽  
Han Yu ◽  
Kun Zhang ◽  
Yuliang Shi ◽  
Cyril Leung ◽  
...  
Keyword(s):  
Decision Support ◽  
Demand Response ◽  
Tracking System ◽  
Electricity Consumption ◽  
Short Term ◽  
Intelligent Decision ◽  
Short Term Load Forecasting ◽  
Information Tracking ◽  
Implementation Management ◽  
Intelligent Decision Support

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

scholarly journals Internet of Things (IoT) Technology for the Development of Intelligent Decision Support Education Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jinhua Liu ◽  
Caiping Wang ◽  
Xianchun Xiao
Keyword(s):  
Internet Of Things ◽  
Online Teaching ◽  
College Education ◽  
Management Platform ◽  
Multimedia Teaching ◽  
Intelligent Decision ◽  
Examination Result ◽  
Online Management ◽  
Education Platform ◽  
Intelligent Decision Support

Improving the intelligence of teaching environment and making the multimedia teaching equipment has become a major concern of the colleges and universities. To this end, the design of Internet of Things (IoT) technology based wisdom of higher education platform is of great interest. Designing the structure of online management platform for college education and realizing the functions of examination result inquiry, online teaching, and attendance management have gained more importance in the educational research. The wisdom classroom is the key structure of the wisdom education platform. A smart classroom architecture based on IoT technology is designed, which connects with traditional network facilities through the IoT gateway. Different layers of the architectures have been designed and implemented. The proposed platform tests results and shows that the intelligent education platform can effectively control classroom utilization and has high throughput, low application latency, and good practicability.

scholarly journals Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3635 ◽  
Author(s):  
Guoming Zhang ◽  
Xiaoyu Ji ◽  
Yanjie Li ◽  
Wenyuan Xu
Keyword(s):  
Smart Grid ◽  
Power Consumption ◽  
Real Time ◽  
Condition Monitoring ◽  
Resource Constraints ◽  
Detection System ◽  
Stable Operation ◽  
Detection Accuracy ◽  
Monitoring Method ◽  
Dsp Processors

As a critical component in the smart grid, the Distribution Terminal Unit (DTU) dynamically adjusts the running status of the entire smart grid based on the collected electrical parameters to ensure the safe and stable operation of the smart grid. However, as a real-time embedded device, DTU has not only resource constraints but also specific requirements on real-time performance, thus, the traditional anomaly detection method cannot be deployed. To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption changes accordingly. To validate this idea, we set up a testbed based on DTU and simulated four types of imperceptible attacks that change the code running in ARM and DSP processors, respectively. We generate representative features and select lightweight ML algorithms to detect these attacks. We finally implemented the detection system on the windows and ubuntu platform and validated its effectiveness. The results show that the detection accuracy is up to 99.98% in a non-intrusive and lightweight way.

A moral analysis of intelligent decision-support systems in diagnostics through the lens of Luciano Floridi’s information ethics

Human Affairs ◽  
2021 ◽  
Vol 31 (2) ◽  
pp. 149-164
Author(s):  
Dmytro Mykhailov
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Medical Diagnosis ◽  
Moral Agency ◽  
Moral Agent ◽  
Well Being ◽  
Medical Diagnostics ◽  
Intelligent Decision ◽  
Luciano Floridi ◽  
Intelligent Decision Support

Abstract Contemporary medical diagnostics has a dynamic moral landscape, which includes a variety of agents, factors, and components. A significant part of this landscape is composed of information technologies that play a vital role in doctors’ decision-making. This paper focuses on the so-called Intelligent Decision-Support System that is widely implemented in the domain of contemporary medical diagnosis. The purpose of this article is twofold. First, I will show that the IDSS may be considered a moral agent in the practice of medicine today. To develop this idea I will introduce the approach to artificial agency provided by Luciano Floridi. Simultaneously, I will situate this approach in the context of contemporary discussions regarding the nature of artificial agency. It is argued here that the IDSS possesses a specific sort of agency, includes several agent features (e.g. autonomy, interactivity, adaptability), and hence, performs an autonomous behavior, which may have a substantial moral impact on the patient’s well-being. It follows that, through the technology of artificial neural networks combined with ‘deep learning’ mechanisms, the IDSS tool achieves a specific sort of independence (autonomy) and may possess a certain type of moral agency. Second, I will provide a conceptual framework for the ethical evaluation of the moral impact that the IDSS may have on the doctor’s decision-making and, consequently, on the patient’s wellbeing. This framework is the Object-Oriented Model of Moral Action developed by Luciano Floridi. Although this model appears in many contemporary discussions in the field of information and computer ethics, it has not yet been applied to the medical domain. This paper addresses this gap and seeks to reveal the hidden potentialities of the OOP model for the field of medical diagnosis.

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