Electrical Energy Consumption Forecast Using External Facility Data

Vinagre, Eugenia; Gomes, Luís; Vale, Zita

doi:10.1109/ssci.2015.101

Cited by 25 publications

(30 citation statements)

References 16 publications

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“…In this section we describe the BD cluster, with a distributed architecture, implemented in a real microgrid infrastructure existent in GECAD research centre [6].…”

Section: A Big Data Platformmentioning

confidence: 99%

Smart Grids Data Management: A Case for Cassandra

Pinheiro

Vinagre

Praça

et al. 2017

Distributed Computing and Artificial Intelligence, 14th International Conference

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Abstract. The objective of this paper is to present a SMACK based platform for microgrids data storage and management. The platform is being used in a real microgrid, with an infrastructure that monitors and controls 3 buildings within the GECAD -ISEP/IPP campus, while, at the same time, receives and manages data sources coming from different types of buildings from associated partners, to whom intelligent services are being provided. Microgrid data comes in different formats, different rates and with an increasing volume, as the microgrid itself covers more customers and areas. Based on the atual available computational resources, a Big Data platform based on the SMACK stack was implemented and is presented. The Cassandra component of the stack has evolved. AC version 2 is still supported until the version 4 release, and is often still used in production environments. However, a new stable version, version 3, introduces major optimizations in the storage that bring disk space savings. The main focus of this work is on the Data Storage and the formalization of the data mapping in Cassandra version 3, which is contextualized by means of a short example with data coming from the monitoring infrastructure of the microgrid.

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“…In this section we describe the BD cluster, with a distributed architecture, implemented in a real microgrid infrastructure existent in GECAD research centre [6].…”

Section: A Big Data Platformmentioning

confidence: 99%

Smart Grids Data Management: A Case for Cassandra

Pinheiro

Vinagre

Praça

et al. 2017

Distributed Computing and Artificial Intelligence, 14th International Conference

Self Cite

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“…The system is implemented in real facilities used by several researchers in a daily basis, aiming the energy monitoring and the energy management inside the laboratories. This system enables the test and use of several communication protocols [18], [19]. The section presents an overview of SOICAM system and a short description of the real office building.…”

Section: Scada Office Intelligent Context Awarenessmentioning

confidence: 99%

“…The simulated facilities use real electrical energy consumption measurements, enabling the profile simulation of the simulated facility. More detailed information can be viewed in [19].…”

Section: Soicam General Structurementioning

confidence: 99%

Dynamic Energy Management Method with Demand Response Interaction Applied in an Office Building

Fernandes

Gomes

Morais

et al. 2016

Advances in Intelligent Systems and Computing

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The intelligent management systems of the end consumers are endowed with advanced functions being one of them the interaction with external entities through the automatic participation in demand response programs. The development of the intelligent management systems is to reduce the energy consumption based on internal information and on the interaction with an external entity. Moreover, the management approaches results in an active participation of the consumers in the operation of the smart grids and microgrids concepts. The paper developed presents the application of a dynamic priority method in SCADA Office Intelligent Context Awareness Management system to manage the energy resources installed in an office building. The intelligent management method allows the dynamic active participation of the office building in the DR events considering the real data of consumption and generation of one building in Polytechnic of Porto. The main goal of the methodology is to obtain a dynamic

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“…The work presented in [7] demonstrates the forecast of electricity consumption by separating the periodic variable and decompositions the pattern. In [8] the author applies an ANN in order to forecast the consumption of an office building.…”

Section: Introductionmentioning

confidence: 99%

Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

Jozi

Pinto

Praça

et al. 2017

2017 IEEE Symposium Series on Computational Intelligence (SSCI)

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Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the out-comesthe actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rulebased systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

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Electrical Energy Consumption Forecast Using External Facility Data

Cited by 25 publications

References 16 publications

Smart Grids Data Management: A Case for Cassandra

Smart Grids Data Management: A Case for Cassandra

Dynamic Energy Management Method with Demand Response Interaction Applied in an Office Building

Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study

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