Construction of energy measuring system in a University for Cluster Energy Management System

Kamiyoshi, Yuto; Nakabe, Tomohisa; Mine, Gouki; Nishi, Hiroaki

doi:10.1109/iecon.2010.5675410

Cited by 13 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CEMS is created using the Keio University Network Oriented Intelligent and Versatile Energy Saving System (KNIVES), a demand-side management system that controls electronic devices based on co-operative and distributed control algorithms and real-time measurement variables [18]. While in [19], the KNIVES system is integrated with a smart circuit box to predict residential power needs.…”

Section: Monitoring At the Building Levelmentioning

confidence: 99%

Energy efficient building automation: A survey paper on approaches and technologies for optimized building operation

Haase

Zucker

Alahmad

2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

Building automation is a trusted technology to ensure user comfort in buildings. Today the challenge is not only to provide comfort, but to do it while having a close eye on the energy performance of the building. Distributed, renewable energy sources, advances in IT and tighter legislative constraints offer and demand changes in the way we operate buildings. We look at the core fields that enable building operation: monitoring of operation data, controls, automation and the management organization to show that innovation can be achieved by integrating cross-domain knowledge and getting inspirations from other domains, where existing approaches and technologies can be used and transferred to the domain of building automation.

show abstract

Section: Monitoring At the Building Levelmentioning

confidence: 99%

Energy efficient building automation: A survey paper on approaches and technologies for optimized building operation

Haase

Zucker

Alahmad

2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

show abstract

“…An adaptive network that forecasts electrical consumptions on a manufacturing plant is shown in [3] as a part of an iEMS. An iEMS that monitors and adjusts energy supply and demand in real-time, in order to form the best combination, was implemented in [4], with the ability to predict the electric power demand based on historical data. A decision-support system was developed in [5] to forecast electricity demand and enable the user to save energy by recommending optimal run times.…”

Section: Introductionmentioning

confidence: 99%

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Sala

Kampouropoulos

Giacometto

et al. 2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

View full text Add to dashboard Cite

A model of power demand represents the foundation of any intelligent Energy Management System, and its accuracy is the key factor determining the performance of such system. In order to improve the accuracy of the modeling process, a multi-model approach based on a Hierarchical Clustering of similar load behaviors is presented. The clustering algorithm joins similar data subsets in groups that are modelled separately using Adaptive Neuro-Fuzzy Inference Systems. Thus, each of the obtained models addresses only the characterization of one behavior, which provides better accuracy than classical approaches based on a single model, in addition to being easier and faster to train. During the training process of the models, an input selection technique based on Genetic Algorithms is proposed to search and select the best combination of inputs. The use of search algorithms allows to reduce the complexity of this task while maintaining the system performance, which represents a significant time saving of expert staff. The proposed approach is validated by means of experimental data from an automotive manufacturing plant. In addition to improving the forecasting accuracy, this methodology automates the segmentation of the load profiles into models and the selection of their inputs, as well as improving parallelization to effectively reduce the computation time.Keywords-adaptive neuro-fuzzy inference systems; demand side management; genetic algorithms; hierarchical clustering; intelligent energy management system.

show abstract

“…The system is unique in that it links several home energy management systems (HEMS) together. CEMS is created using the Keio University Network Oriented Intelligent and Versatile Energy Saving System (KNIVES), a demand side management system that controls electronic devices based on co-operative and distributed control algorithms and real-time measurement variables [7]. In [8], the authors integrate the KNIVES system with a smart circuit box to predict residential power needs.…”

Section: Introductionmentioning

confidence: 99%

Technologies in the Built Environment to Enable Sustainability and Energy Conservation

Alahmad¹,

Sharif²,

Wisnieski³

et al. 2012

IJI

View full text Add to dashboard Cite

show abstract

Construction of energy measuring system in a University for Cluster Energy Management System

Cited by 13 publications

References 6 publications

Energy efficient building automation: A survey paper on approaches and technologies for optimized building operation

Energy efficient building automation: A survey paper on approaches and technologies for optimized building operation

Smart multi-model approach based on adaptive Neuro-Fuzzy Inference Systems and Genetic Algorithms

Technologies in the Built Environment to Enable Sustainability and Energy Conservation

Contact Info

Product

Resources

About