António Andrade-Campos

António Andrade-Campos

orcid

Research Focus & Metrics

Academic Career Length

23Years (2004-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 February 2014 in Renewable and Sustainable Energy Reviews
Verified
Journal Article
Closed access
BCBernardete Coelho
Bernardete Coelho
AAAntónio Andrade-Campos
António Andrade-Campos

1 September 2018 in International Journal of Mechanical Sciences
Verified
Journal Article
Closed access
JMJoão M. P. Martins
João M. P. Martins
AAAntónio Andrade-Campos
António Andrade-Campos
STS. Thuillier
S. Thuillier

1 August 2007 in International Journal of Plasticity
Verified
Journal Article
Closed access
AAAntónio Andrade-Campos
António Andrade-Campos
STSandrine Thuillier
Sandrine Thuillier
PPP. Pilvin
P. Pilvin
Filipe Teixeira-DiasFilipe Teixeira-Dias
Filipe Teixeira-Dias

21 August 2018 in Journal of Hydroinformatics
Verified
Journal Article
Open access
PDF file
AAA. Antunes
A. Antunes
AAAntónio Andrade-Campos
António Andrade-Campos
ASA. Sardinha-Lourenço
A. Sardinha-Lourenço
MOMiguel Silva Oliveira
Miguel Silva Oliveira
Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

Verified
Journal Article
Open access
PDF file
BBB. Barroqueiro
B. Barroqueiro
AAAntónio Andrade-Campos
António Andrade-Campos
Robertt ValenteRobertt Valente
Robertt Valente
Victor NetoVictor Neto
Victor Neto
Additive Manufacturing (AM) is the forefront of advanced manufacturing technologies and has the potential to revolutionize manufacturing, with a dramatic change in the design and project paradigms. A comprehensive review of existent metal AM processes, processable materials, respective defects and inspection methods (destructive and non-destructive) is presented in a succinct manner. Particularly, the AM design optimization methodologies are reviewed and their threats and constraints discussed. Finally, an aerospace industry case study is presented and several cost-effective examples are enumerated.

1 November 2019 in International Journal of Solids and Structures
Verified
Journal Article
Closed access
JMJoão M. P. Martins
João M. P. Martins
AAAntónio Andrade-Campos
António Andrade-Campos
STSandrine Thuillier
Sandrine Thuillier