From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

2018 ◽  
Author(s):  
Colm V. Gallagher ◽  
Peter OrDonovan ◽  
Kevin Leahy ◽  
Ken Bruton ◽  
Dominic T.J. OrSullivan
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Energy Savings ◽  
Demand Side ◽  
Performance Verification ◽  
Time Performance

scholarly journals Demand-side management strategies based on energy key perfomance indicators in real-time: Case study

2020 ◽  
Vol 10 (1) ◽  
pp. 5-16
Author(s):  
Sandra Milena Tellez- Gutierrez ◽  
Oscar German Duarte Velasco ◽  
Javier Rosero García
Keyword(s):  
Real Time ◽  
Performance Indicators ◽  
Energy Savings ◽  
New Technologies ◽  
Management Strategies ◽  
Demand Side Management ◽  
Key Performance Indicators ◽  
Demand Side ◽  
Advanced Metering

This paper sets out features of traditional Energy Key Performance Indicators (KPIs) employed in energy management programs; then, new indicators are proposed based on Advanced Metering Infrastructure (AMI) usage. These indicators make it possible to directly relate the amount of energy, type of end use and user consumption patterns. Analysis of AMI system information enables planning for differentiated Demand-Side Management (DSM) strategies. A case study developed at Universidad Nacional de Colombia - Bogotá campus is presented, which proposes new Energy Key Performance Indicators in Real Time. These indicators enable information analysis and DSM strategies that are appropriate for new technologies and that are aimed at increasing energy efficiency. Additionally, this paper presents the factors that have to be taken into account when implementing KPIs (Key Performance Indicators) and the decision-making process. This results in variable overall energy savings between 5 and 40%, according to the DSM strategy implemented.

Real-time Performance Monitoring of MPC Based on Covariance Index Prediction

2014 ◽  
Vol 39 (5) ◽  
pp. 658-663 ◽  
Author(s):  
Xue-Min TIAN ◽  
Ya-Jie SHI ◽  
Yu-Ping CAO
Keyword(s):  
Real Time ◽  
Performance Monitoring ◽  
Time Performance

scholarly journals BattleNet: Capturing Advantageous Battlefield in RTS Games (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13849-13850
Author(s):  
Donghyeon Lee ◽  
Man-Je Kim ◽  
Chang Wook Ahn
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Real Time ◽  
Large Scale ◽  
Outcome Predictor ◽  
Short Term ◽  
Rts Game

In a real-time strategy (RTS) game, StarCraft II, players need to know the consequences before making a decision in combat. We propose a combat outcome predictor which utilizes terrain information as well as squad information. For training the model, we generated a StarCraft II combat dataset by simulating diverse and large-scale combat situations. The overall accuracy of our model was 89.7%. Our predictor can be integrated into the artificial intelligence agent for RTS games as a short-term decision-making module.

Single Depth View Based Real-Time Reconstruction of Hand-Object Interactions

2021 ◽  
Vol 40 (3) ◽  
pp. 1-12
Author(s):  
Hao Zhang ◽  
Yuxiao Zhou ◽  
Yifei Tian ◽  
Jun-Hai Yong ◽  
Feng Xu
Keyword(s):  
Real Time ◽  
Synthetic Data ◽  
Real Data ◽  
Depth Image ◽  
Real Time System ◽  
The Real ◽  
Time Performance ◽  
Contact Constraint ◽  
Object Shapes ◽  
Object Interactions

Reconstructing hand-object interactions is a challenging task due to strong occlusions and complex motions. This article proposes a real-time system that uses a single depth stream to simultaneously reconstruct hand poses, object shape, and rigid/non-rigid motions. To achieve this, we first train a joint learning network to segment the hand and object in a depth image, and to predict the 3D keypoints of the hand. With most layers shared by the two tasks, computation cost is saved for the real-time performance. A hybrid dataset is constructed here to train the network with real data (to learn real-world distributions) and synthetic data (to cover variations of objects, motions, and viewpoints). Next, the depth of the two targets and the keypoints are used in a uniform optimization to reconstruct the interacting motions. Benefitting from a novel tangential contact constraint, the system not only solves the remaining ambiguities but also keeps the real-time performance. Experiments show that our system handles different hand and object shapes, various interactive motions, and moving cameras.

scholarly journals Design of a dynamic and self-adapting system, supported with artificial intelligence, machine learning and real-time intelligence for predictive cyber risk analytics in extreme environments – cyber risk in the colonisation of Mars

2021 ◽  
Author(s):  
Petar Radanliev ◽  
David De Roure ◽  
Kevin Page ◽  
Max Van Kleek ◽  
Omar Santos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Real Time ◽  
Cyber Security ◽  
Extreme Environments ◽  
Learning Technologies ◽  
Cyber Attacks ◽  
Edge Computing ◽  
Mathematical Formulas ◽  
Cyber Risk

AbstractMultiple governmental agencies and private organisations have made commitments for the colonisation of Mars. Such colonisation requires complex systems and infrastructure that could be very costly to repair or replace in cases of cyber-attacks. This paper surveys deep learning algorithms, IoT cyber security and risk models, and established mathematical formulas to identify the best approach for developing a dynamic and self-adapting system for predictive cyber risk analytics supported with Artificial Intelligence and Machine Learning and real-time intelligence in edge computing. The paper presents a new mathematical approach for integrating concepts for cognition engine design, edge computing and Artificial Intelligence and Machine Learning to automate anomaly detection. This engine instigates a step change by applying Artificial Intelligence and Machine Learning embedded at the edge of IoT networks, to deliver safe and functional real-time intelligence for predictive cyber risk analytics. This will enhance capacities for risk analytics and assists in the creation of a comprehensive and systematic understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when Artificial Intelligence and Machine Learning technologies are migrated to the periphery of the internet and into local IoT networks.

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