Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

scholarly journals Ambient intelligence for optimal manufacturing and energy efficiency

2015 ◽  
Vol 35 (3) ◽  
pp. 234-248 ◽  
Author(s):  
David Charles Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Management ◽  
Ambient Intelligence ◽  
Energy Use ◽  
Management Systems ◽  
Content Type ◽  
Energy Management Systems ◽  
Knowledge Based ◽  
Comprehensive Information

Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.

scholarly journals Evaluating the Economic and Environmental Impacts of Smart Management Systems for Cooling and Heating Systems in Building: Case study of Office Building in Tehran

2021 ◽  
Author(s):  
Gholamreza Heravi ◽  
Milad Rostami ◽  
Maryam Shekari
Keyword(s):  
Energy Consumption ◽  
Environmental Impacts ◽  
Energy Management ◽  
Building Energy ◽  
Office Building ◽  
Management Systems ◽  
Energy Management Systems ◽  
Building Energy Management ◽  
Operation Period

Considering the increasing rate of energy consumption and its environmental detrimental effects, as well as considering the use of non-renewable energy sources such as fossil fuels, energy management issues have become more important. Given the 40% share of the building industry's total energy consumption, as well as the 80% share of energy consumed during the operation period, attention to the areas of energy management and optimization during the operation period of the buildings can have a major impact on buildings’ energy performance. In this research, through identifying building energy management tools and studying previous studies and assessing the effects of building energy management systems, the economic and environmental impacts of using building energy management systems on the annual energy consumption in an office building in Tehran as a case study has been investigated. The results indicate a 32 percent reduction in energy consumption and a significant reduction in the release of the environmental pollutants in smart mode compared to the base mode. Moreover, considering the social costs associated with the emitted pollutants as well as the return period, it has been attempted to identify the factors contributing to the economic justification of using smart heating and cooling systems. According to the results, the use of smart energy management systems can be considered as an effective step in optimizing and managing energy consumption in the construction sector.

Contemporary Energy Management Systems and Future Prospects

2021 ◽  
pp. 1984-2013
Author(s):  
Amir Manzoor
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
User Participation ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Systems ◽  
Research Areas ◽  
Emerging Trends

The transformation of electric grid into smart grid has improved management of available resources and increased energy efficiency. Energy management systems (EMS) play an important role in enhancing user participation in control of energy management. Using such systems, consumers can obtain information about their energy consumption patterns and shape their energy consumption behaviors for efficient energy utilization. Contemporary EMS utilizes advanced analytics and ICT to provide consumers actionable feedback and control of energy management. These systems provide high availability, an easy-to-use user interface, security, and privacy. This chapter explores the contemporary EMS, their applications, classifications, standards, and frameworks. The chapter defines a set of requirements for EMS and provides feature comparison of various EMS. The chapter also discusses emerging trends and future research areas in EMS.

scholarly journals A Residential House Comparative Case Study Using Market Available Smart Plugs and EnAPlugs with Shared Knowledge

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1647 ◽  
Author(s):  
Luis Gomes ◽  
Filipe Sousa ◽  
Tiago Pinto ◽  
Zita Vale
Keyword(s):  
Energy Management ◽  
Management Systems ◽  
Comparative Case Study ◽  
Shared Knowledge ◽  
Monitoring And Control ◽  
Energy Management Systems ◽  
Multi Agent ◽  
Residential House ◽  
And Control

Smart home devices currently available on the market can be used for remote monitoring and control. Energy management systems can take advantage of this and deploy solutions that can be implemented in our homes. One of the big enablers is smart plugs that allow the control of electrical resources while providing a retrofitting solution, hence avoiding the need for replacing the electrical devices. However, current so-called smart plugs lack the ability to understand the environment they are in, or the electrical appliance/resource they are controlling. This paper applies environment awareness smart plugs (EnAPlugs) able to provide enough data for energy management systems or act on its own, via a multi-agent approach. A case study is presented, which shows the application of the proposed approach in a house where 17 EnAPlugs are deployed. Results show the ability to shared knowledge and perform individual resource optimizations. This paper evidences that by integrating artificial intelligence on devices, energy advantages can be observed and used in favor of users, providing comfort and savings.

Using energy profiles to identify university energy reduction opportunities

2016 ◽  
Vol 17 (2) ◽  
pp. 188-207 ◽  
Author(s):  
Nandarani Maistry ◽  
Harold Annegarn
Keyword(s):  
South Africa ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Quantitative Research ◽  
Electricity Consumption ◽  
Working Hours ◽  
Content Type ◽  
Academic Calendar ◽  
The University

Purpose – The purpose of this paper is to outline efforts at the University of Johannesburg, a large metropolitan university in Gauteng province, to examine energy efficiency within the context of the green campus movement, through the analysis of electricity consumption patterns. The study is particularly relevant in light of the cumulative 230 per cent increase in electricity costs between 2008 and 2014 in South Africa that has forced institutions of higher education to seek ways to reduce energy consumption. Design/Methodology/Approach – A quantitative research design was adopted for the analysis of municipal electricity consumption records using a case study approach to identify trends and patterns in consumption. The largest campus of the University of Johannesburg, which is currently one of the largest residential universities in South Africa, was selected as a case study. Average diurnal consumption profiles were plotted according to phases of the academic calendar, distinguished by specific periods of active teaching and research (in-session); study breaks, examinations and administration (out-of-session); and recesses. Average profiles per phase of the academic calendar were constructed from the hourly electricity consumption and power records using ExcelTM pivot tables and charts. Findings – It was found that the academic calendar has profound effects on energy consumption by controlling the level of activity. Diurnal maximum consumption corresponds to core working hours, peaking at an average of 2,500 kWh during “in-session” periods, 2,250 kWh during “out-of-session” periods and 2,100 kWh during recess. A high base load was evident throughout the year (between 1,300 and 1,650 kWh), mainly attributed to heating and cooling. By switching off the 350 kW chiller plant on weekdays, a 9 per cent electricity reduction could be achieved during out-of-session and recess periods. Similarly, during in-session periods, a 6 per cent reduction could be achieved. Practical implications – Key strategies and recommendations are presented to stimulate energy efficiency implementation within the institution. Originality Value – Coding of consumption profiles against the academic calendar has not been previously done in relation to an academic institution. The profiles were used to establish the influence of the academic calendar on electricity consumption, which along with our own observation were used to identify specific consumption reduction opportunities worth pursuing.

scholarly journals Implementation of an Energy Management System in Colombian Manufacturing - A Methodological Approach

2021 ◽  
Vol 2 (2) ◽  
pp. 9-14
Author(s):  
Juan David Marín García ◽  
Juan David Marin Jimenez ◽  
Sandra Ximena Carvajal Quintero
Keyword(s):  
Energy Efficiency ◽  
Energy Management ◽  
Industry 4.0 ◽  
Systems Approach ◽  
Methodological Approach ◽  
Management Systems ◽  
Energy Management System ◽  
Advanced Metering Infrastructure ◽  
Energy Management Systems ◽  
Advanced Metering

This paper aims to analyze mechanisms such as the Energy management systems approach in industry 4.0. The paper is a review of techniques for optimizing energy consumption with energy efficiency, advanced metering infrastructure and rational and efficient use of energy to reduce the pollution as well as to strengthen Industry 4.0 models and the monitoring and management opportunities that exist with the implementation of this models in Colombia.

scholarly journals Design of Ensemble Forecasting Models for Home Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7664
Author(s):  
Karol Bot ◽  
Samira Santos ◽  
Inoussa Laouali ◽  
Antonio Ruano ◽  
Maria da Graça Ruano
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Simple Procedure ◽  
Ensemble Forecasting ◽  
Management Systems ◽  
Energy Forecasting ◽  
Energy Management Systems ◽  
Ensemble Techniques ◽  
Home Energy Management ◽  
Home Energy Management System

The increasing levels of energy consumption worldwide is raising issues with respect to surpassing supply limits, causing severe effects on the environment, and the exhaustion of energy resources. Buildings are one of the most relevant sectors in terms of energy consumption; as such, efficient Home or Building Management Systems are an important topic of research. This study discusses the use of ensemble techniques in order to improve the performance of artificial neural networks models used for energy forecasting in residential houses. The case study is a residential house, located in Portugal, that is equipped with PV generation and battery storage and controlled by a Home Energy Management System (HEMS). It has been shown that the ensemble forecasting results are superior to single selected models, which were already excellent. A simple procedure was proposed for selecting the models to be used in the ensemble, together with a heuristic to determine the number of models.

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