J
Joao P. S. Catalao
Researcher at University of Porto
Publications - 1115
Citations - 28139
Joao P. S. Catalao is an academic researcher from University of Porto. The author has contributed to research in topics: Demand response & Wind power. The author has an hindex of 68, co-authored 1039 publications receiving 19348 citations. Previous affiliations of Joao P. S. Catalao include Instituto Superior de Engenharia de Lisboa & University of Lisbon.
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Journal ArticleDOI
An overview of Demand Response: Key-elements and international experience
Nikolaos G. Paterakis,Ozan Erdinc,Ozan Erdinc,Joao P. S. Catalao,Joao P. S. Catalao,Joao P. S. Catalao +5 more
TL;DR: In this article, a complete and up-to-date overview of demand response (DR) enabling technologies, programs and consumer response types is presented, as well as the benefits and the drivers that have motivated the adoption of DR programs and the barriers that may hinder their further development.
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A Review of Smart Cities Based on the Internet of Things Concept
Saber Talari,Miadreza Shafie-khah,Pierluigi Siano,Vincenzo Loia,Aurelio Tommasetti,Joao P. S. Catalao +5 more
TL;DR: Some practical experiences all across the world and the key barriers to its implementation are thoroughly expressed and the potential application of smart cities with respect to technology development in the future provides another valuable discussion in this paper.
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Short-term electricity prices forecasting in a competitive market: A neural network approach
TL;DR: In this article, a neural network approach for forecasting short-term electricity prices is proposed. But the authors focus on the short term and do not consider the long-term forecast of electricity prices, and use a three-layered feed-forward neural network for forecasting next-week electricity prices.
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Smart Household Operation Considering Bi-Directional EV and ESS Utilization by Real-Time Pricing-Based DR
Ozan Erdinc,Nikolaos G. Paterakis,Tiago D. P. Mendes,Anastasios G. Bakirtzis,Joao P. S. Catalao +4 more
TL;DR: A collaborative evaluation of dynamic-pricing and peak power limiting-based DR strategies with a bi-directional utilization possibility for EV and energy storage system (ESS) is realized and a mixed-integer linear programming (MILP) framework-based modeling of a home energy management (HEM) structure is provided.
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Optimal Household Appliances Scheduling Under Day-Ahead Pricing and Load-Shaping Demand Response Strategies
TL;DR: A detailed home energy management system structure is developed to determine the optimal day-ahead appliance scheduling of a smart household under hourly pricing and peak power-limiting (hard and soft power limitation)-based demand response strategies.