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Aria Jozi
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2020 – today
- 2023
- [c10]Aria Jozi, Tiago Pinto, Luis Gomes, Goreti Marreiros, Zita Vale:
Rule-Based System for Intelligent Energy Management in Buildings. EPIA (2) 2023: 169-181
2010 – 2019
- 2019
- [j1]Luis Gomes, Carlos Ramos, Aria Jozi, Bruno Serra, Lucas Paiva, Zita A. Vale:
IoH: A Platform for the Intelligence of Home with a Context Awareness and Ambient Intelligence Approach. Future Internet 11(3): 58 (2019) - [c9]Aria Jozi, Daniel Ramos, Luis Gomes, Pedro Faria, Tiago Pinto, Zita A. Vale:
Demonstration of an Energy Consumption Forecasting System for Energy Management in Buildings. EPIA (1) 2019: 462-468 - 2018
- [c8]Aria Jozi, Tiago Pinto, Isabel Praça, Zita A. Vale, João P. Soares:
Day ahead electricity consumption forecasting with MOGUL learning model. IJCNN 2018: 1-6 - [c7]Aria Jozi, Tiago Pinto, Isabel Praça, Zita A. Vale:
Day-ahead forecasting approach for energy consumption of an office building using support vector machines. SSCI 2018: 1620-1625 - 2017
- [c6]Amin Shokri Gazafroudi, Tiago Pinto, Francisco Prieto Castrillo, Javier Prieto, Juan Manuel Corchado, Aria Jozi, Zita A. Vale, Ganesh Kumar Venayagamoorthy:
Organization-based Multi-Agent structure of the Smart Home Electricity System. CEC 2017: 1327-1334 - [c5]Amin Shokri Gazafroudi, Tiago Pinto, Francisco Prieto Castrillo, Juan Manuel Corchado, Omid Abrishambaf, Aria Jozi, Zita A. Vale:
Energy flexibility assessment of a multi agent-based smart home energy system. ICUWB 2017: 1-7 - [c4]Amin Shokri Gazafroudi, Francisco Prieto Castrillo, Tiago Pinto, Aria Jozi, Zita A. Vale:
Economic Evaluation of Predictive Dispatch Model in MAS-Based Smart Home. PAAMS (Special Sessions) 2017: 81-91 - [c3]Aria Jozi, Tiago Pinto, Isabel Praça, Sérgio Ramos, Zita A. Vale, Bénédicte Goujon, Teodora Petrisor:
Energy consumption forecasting using neuro-fuzzy inference systems: Thales TRT building case study. SSCI 2017: 1-5 - 2016
- [c2]Aria Jozi, Tiago Pinto, Isabel Praça, Francisco Silva, Brigida Teixeira, Zita A. Vale:
Wang and Mendel's fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature. GIIS 2016: 1-6 - [c1]Aria Jozi, Tiago Pinto, Isabel Praça, Francisco Silva, Brigida Teixeira, Zita A. Vale:
Energy consumption forecasting based on Hybrid Neural Fuzzy Inference System. SSCI 2016: 1-5
Coauthor Index
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