Integrated Platform for Smart Operational Monitoring and Efficient Energy Management of Water Supply Networks

2021 ◽  
Author(s):  
Anastasios Perdios ◽  
Georgia Papacharalampous ◽  
Athanasios Dimas ◽  
Georgios Horsch ◽  
Irene Karathanasi ◽  
...  
Keyword(s):  
Real Time ◽  
Energy Management ◽  
The European Union ◽  
Time Data ◽  
Environmental Indices ◽  
Development Fund ◽  
Operational Monitoring ◽  
Pumping Stations ◽  
Pumping Wells ◽  
Integrated Platform

<p>Research project “PerManeNt” aims at developing an integrated platform for operational monitoring, smart control, and sustainable energy management of the external aqueduct system of the city of Patras in western Greece, which consists of more than 60 km of pressurized pipeline, 44 pumping wells, 3 springs, 22 regulating tanks, and 14 pumping stations. Given the significance of the existing infrastructure, 5 main pipelines, 7 pumping wells, 9 reservoirs, and 5 pumping stations were selected to be monitored in the context of: a) real-time data collection, processing and visualization, b) near real-time detection of system malfunctioning and automatic alarm generation, and c) generation of short and longer term forecasts for the water demand and corresponding energy consumption rates, based on hydrometeorological data and environmental indices. The development of the integrated platform is expected to have significant scientific, financial, societal and environmental impacts including: i) efficient water resources management and environmental protection, ii) reduction of the operational costs and regulator expenses for system maintenance and management, iii) promotion of citizens’ awareness regarding environmental issues, and iv) significant improvement of the quality of services offered, including pricing and emergency planning.</p><p><strong>Acknowledgments:</strong></p><p>This research is co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-4177.</p>

Online Data Compression Technique for Real Time Data of Energy Management System in the Industrial Production

2014 ◽  
Vol 519-520 ◽  
pp. 70-73 ◽  
Author(s):  
Jing Bai ◽  
Tie Cheng Pu
Keyword(s):  
Data Compression ◽  
Real Time ◽  
Energy Management ◽  
Industrial Production ◽  
Energy Management System ◽  
Sampled Data ◽  
Time Data ◽  
Online Data ◽  
Compression Technique ◽  
Real Time Data

Aiming at storing and transmitting the real time data of energy management system in the industrial production, an online data compression technique is proposed. Firstly, the auto regression model of a group of sequence is established. Secondly, the next sampled data can be predicted by the model. If the estimated error is in the allowable range, we save the parameters of model and the beginning data. Otherwise, we save the data and repeat the method from the next sampled data. At Last, the method is applied in electricity energy data compression of a beer production. The application result verifies the effectiveness of the proposed method.

IOT and Industry 4.0 – Gateway towards improving Energy Efficiency

2017 ◽  
Vol 7 (9) ◽  
pp. 58
Author(s):  
Jait Purohit
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Real Time ◽  
Energy Management ◽  
Manufacturing Industry ◽  
Management Practices ◽  
Data Gathering ◽  
Time Data ◽  
Improve Energy Efficiency ◽  
Day To Day Operations

Energy efficiency (EE) has become an important benchmark in manufacturing industry due the increasing concerns about climate change and tightening of environmental regulations. However, most manufacturing and production industries today are only able to monitor aggregated energy consumption and lack the real-time visibility of EE on the shop floors. The ability to access energy information and effectively analyse such real-time data to extract key indicators is a crucial factor for successful energy management. While enabling real-time online monitoring of Energy Efficiency, it also applies data gathering analysis to detect abnormal energy consumption patterns and quantify energy efficiency gaps. Through a case study of a microfluidic device manufacturing line, we demonstrate how the application can assist energy managers in embedding best energy management practices in their day-to-day operations and improve Energy Efficiency by eliminating possible energy wastages on manufacturing shop floors.

scholarly journals BIM-Based Human Machine Interface (HMI) Framework for Energy Management

2020 ◽  
Vol 12 (21) ◽  
pp. 8861
Author(s):  
Taewook Kang
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Control Point ◽  
Heterogeneous Systems ◽  
Human Machine Interface ◽  
Information Modeling ◽  
Time Data ◽  
Positive Effects ◽  
Building Energy Management ◽  
Machine Interface

This study proposes a Building Information Modeling (BIM)-based Human Machine Interface (HMI) framework for intuitive space-based energy management. The BIM-based HMI supports building managers with a method of linking data between BIM and Building Energy Management System (BEMS), which are heterogeneous systems, and provides space-based real-time energy monitoring. This study also proposes a BIM and BEMS data linking framework for systematic BIM-based HMI development. Towards this end, the BIM-based HMI framework was defined after deriving the considerations and requirements necessary for linking the energy control point and BIM through a questionnaire designed by practitioners. Through case analysis, the authors implemented BIM-based HMI and analyzed its effects. The results of the analysis confirmed the positive effects (3.9/5.0) on the connectivity of BIM-based HMI, the benefits (4.3/5.0) for real-time data monitoring, the system function expandability, and the BIM-based spatial intuitiveness.

scholarly journals Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 531
Author(s):  
Yujian Ye ◽  
Dawei Qiu ◽  
Huiyu Wang ◽  
Yi Tang ◽  
Goran Strbac
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Demand Response ◽  
Superior Performance ◽  
Management Approach ◽  
Management Problem ◽  
Time Data ◽  
Model Free ◽  
Home Energy Management ◽  
Policy Gradient

With the roll-out of smart meters and the increasing prevalence of distributed energy resources (DERs) at the residential level, end-users rely on home energy management systems (HEMSs) that can harness real-time data and employ artificial intelligence techniques to optimally manage the operation of different DERs, which are targeted toward minimizing the end-user’s energy bill. In this respect, the performance of the conventional model-based demand response (DR) management approach may deteriorate due to the inaccuracy of the employed DER operating models and the probabilistic modeling of uncertain parameters. To overcome the above drawbacks, this paper develops a novel real-time DR management strategy for a residential household based on the twin delayed deep deterministic policy gradient (TD3) learning approach. This approach is model-free, and thus does not rely on knowledge of the distribution of uncertainties or the operating models and parameters of the DERs. It also enables learning of neural-network-based and fine-grained DR management policies in a multi-dimensional action space by exploiting high-dimensional sensory data that encapsulate the uncertainties associated with the renewable generation, appliances’ operating states, utility prices, and outdoor temperature. The proposed method is applied to the energy management problem for a household with a portfolio of the most prominent types of DERs. Case studies involving a real-world scenario are used to validate the superior performance of the proposed method in reducing the household’s energy costs while coping with the multi-source uncertainties through comprehensive comparisons with the state-of-the-art deep reinforcement learning (DRL) methods.

Energy Management System for Domestic Electrical Appliances

2012 ◽  
Vol 3 (4) ◽  
pp. 48-60 ◽  
Author(s):  
Kuo-Ming Chao ◽  
Nazaraf Shah ◽  
Raymond Farmer ◽  
Adriana Matei
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Management ◽  
Large Volume ◽  
Management Systems ◽  
Time Data ◽  
Consumption Behavior ◽  
Data Services ◽  
Energy Management Systems ◽  
Reduce Energy Consumption

A variety of energy management systems are currently available for domestic domain, and many are concerned with real-time energy consumption monitoring and display of statistical and real time data of energy consumption. Although these systems play a crucial role in providing a detailed picture of energy consumption in home environment and contribute to influencing energy consumption behavior, households are required to then take appropriate measures to reduce energy consumption. Some energy management systems provide energy saving tips but they do not take into account households’ profiles and energy consumption of home appliances. To generate an effective and real time appliance level advice on energy consumption, the system must be able to cope with a large volume of data. The proposed system addresses this issue by taking into account household profiles and energy consumption of domestic electrical appliances. The system also uses an approach based on functional data services to deal with the challenge of processing a large volume of data in real time.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

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