Publicacoes - INESC TEC

Publicações

Publicações por HumanISE

2023

Rule-Based System for Intelligent Energy Management in Buildings

Autores
Jozi, A; Pinto, T; Gomes, L; Marreiros, G; Vale, Z;

Publicação
Progress in Artificial Intelligence - 22nd EPIA Conference on Artificial Intelligence, EPIA 2023, Faial Island, Azores, September 5-8, 2023, Proceedings, Part II

Abstract

2023

Study of Forecasting Methods' Impact in Wholesale Electricity Market Participation

Autores
Teixeira, B; Faia, R; Pinto, T; Vale, Z;

Publicação
Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference, Guimaraes, Portugal, 12-14 July 2023.

Abstract

2023

Dynamic Parameterization of Metaheuristics Using a Multi-agent System for the Optimization of Electricity Market Participation

Autores
Carvalho, J; Pinto, T; Home Ortiz, JM; Teixeira, B; Vale, Z; Romero, R;

Publicação
Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference, Guimaraes, Portugal, 12-14 July 2023.

Abstract

2023

Distributed Computing and Artificial Intelligence, Special Sessions I, 20th International Conference, Guimaraes, Portugal, 12-14 July 2023

Autores
Mehmood, R; Alves, V; Praça, I; Wikarek, J; Domínguez, JP; Loukanova, R; Miguel, Id; Pinto, T; Nunes, R; Ricca, M;

Publicação
DCAI (2)

Abstract

2023

Editorial: Explainability in knowledge-based systems and machine learning models for smart grids

Autores
Santos, G; Pinto, T; Ramos, C; Corchado, JM;

Publicação
FRONTIERS IN ENERGY RESEARCH

Abstract
[No abstract available]

FecharLer Abstract

2023

Application of XAI-based framework for PV Energy Generation Forecasting

Autores
Teixeira, B; Carvalhais, L; Pinto, T; Vale, Z;

Publicação
2023 IEEE CONFERENCE ON ARTIFICIAL INTELLIGENCE, CAI

Abstract
The structural changes in the energy sector caused by renewable sources and digitization have resulted in an increased use of Artificial Intelligence (AI), including Machine Learning (ML) models. However, these models' black-box nature and complexity can create issues with transparency and trust, thereby hindering their interpretability. The use of Explainable AI (XAI) can offer a solution to these challenges. This paper explores the application of an XAI-based framework to analyze and evaluate a photovoltaic energy generation forecasting problem and contribute to the trustworthiness of ML solutions.

FecharLer Abstract