Henrique Pombeiro - Academia.edu (original) (raw)

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Papers by Henrique Pombeiro

Sustainable Energy, Grids and Networks

Sustainable Cities and Society, 2019

Studies have shown that aspects relating to user behavior play a decisive role in energy consumpt... more Studies have shown that aspects relating to user behavior play a decisive role in energy consumption reduction, namely in the residential ore, it is expected that domestic reporting systems will elicit user support in identifying sources of inefficient energy usage, leading them to identify and implement both preventive and corrective actions. However, these systems have shortcomings in features encouraging User Behavior Transformation (UBT); how to design domestic reporting user interfaces in a way that effectively triggers UBT still remains an open issue. This article discusses relevant aspects of UBT and analyzes related work in the area, identifying a set of design principles to be applied when developing user interfaces towards residential energy consumers. A thorough evaluation of UBT-enabling design dimensions of current solutions for the domestic sector is undertaken and our conclusions reinforce that these systems still fall behind in addressing UBT, namely when considering social dimensions.

Energy and Buildings, 2017

Sustainable Cities and Society, 2017

Finding the optimal balance between electricity demand and production constrained to economic and... more Finding the optimal balance between electricity demand and production constrained to economic and comfort variables requires intelligent decision and control. This article addresses the formulation of three models that optimize control of a heating, ventilation and air conditioning (HVAC) system in an experimental room, which are coupled with two thermal models of the indoor temperature. Electricity is supplied by the grid and a photovoltaic system with batteries. The primary objective is to maximize users comfort while minimizing cost constrained to: thermal comfort; variable electricity price; and available electricity in batteries that are charged by a PV system. Three models are developed: (i) dynamic programming with simplified thermal model (STM), (ii) genetic algorithm with STM, and (iii) genetic algorithm with EnergyPlus. The genetic algorithm model that uses EnergyPlus to simulate indoor temperature generally achieves higher convergence to the optimal value, which also is the one that uses more electricity from the PV system to operate the HVAC. The dynamic programming performs better than the genetic algorithm (both coupled with STM). However, it is limited by the fact that uses STM, which is a less accurate model to simulate indoor temperature especially because it is not considering thermal inertia.

2016 Future Technologies Conference (FTC), 2016

This article describes the main considerations when designing a feedback platform for encouraging... more This article describes the main considerations when designing a feedback platform for encouraging end users on adopting more efficient consumption behaviors. A review on behavioral models and the main characteristics of current feedback systems is performed, being completed with a description of an experimental case on 472 households in Portugal to which a feedback platform has been developed to raise awareness towards more efficient energy consumption habits. The design process of the platform is described, in which early findings show that it is not enough to design a platform with features that may increase usability but, more importantly, a hypothesis is outlined premising that a careful and tailored communication strategy should be performed to encourage end users on interacting with the platform, thus engaging into more efficient consumption behaviors. This article contributes to solidify the knowledge base of energy efficiency behavior, which so often regards separately different sciences such as behavior economics, engineering or social sciences, leading to a high sparsity of conclusions on this topic.

World Sustainability Series, 2016

The Smart Campus is a European project based in four pilots located in Helsinki (Metropolia Unive... more The Smart Campus is a European project based in four pilots located in Helsinki (Metropolia University), Lisbon (Instituto Superior Tecnico), Lulea (Technology University) and Milan (Politecnico di Milano), having engaged 76,000 users since August 2012 during 33 months. The main objective of this project was to demonstrate the central role of Information and Communication Technology (ICT) based services that act upon Energy Management Systems and control heating, ventilation and air conditioning (HVAC) and Lighting on achieving energy efficiency through dynamically negotiating building environmental conditions with the users on the University pilots. This paper presents the energy saving results and the best practices obtained in the Lisbon Pilot where ICT equipment was installed in a Library, a lecture Amphitheater and a set of offices. Overall savings were almost 40 % in some test locations. It was observed that energy savings were different between test locations due to particularities such as space typology, occupation and utilization patterns, equipment installed, HVAC and lighting control systems and users interaction.

2014 Science and Information Conference, 2014

This paper presents a comparative study of modelling a baseline of electricity consumption in two... more This paper presents a comparative study of modelling a baseline of electricity consumption in two experimental spaces in a Portuguese University campus: one amphitheater and one library. Five input variables were defined for the study: occupation, day length, solar radiation, solar radiation from the previous day and heating and cooling degree days. Current performance and verification protocols accept linear regression models to quantify savings. We present neuro-fuzzy models as an alternative since energy consumption may not be described only by linear models. For each space, a linear regression model was applied, followed by three fuzzy models and one neural network model. The performance of each is assessed and compared in this work. Linear regression and fuzzy models are considered as less adequate to describe electricity consumption under the experimental setups after analyzing the performance indexes and the study of the output profiles. Neural network models give better performance indexes, although they still result in low VAFs equal to 40.4% for the amphitheater, 44.3% for the library in the warm season and 55.8% in the cold season. Constraints were identified such as the precision of the occupation characterization and new models are proposed that take into account the identification of discrete events.

ceur-ws.org

Abstract. The identification of energy consumption patterns contributes for the tailoring of ener... more Abstract. The identification of energy consumption patterns contributes for the tailoring of energy efficiency solutions. This paper contributes to this issue by addressing the characterization of electricity consumption data with 15 min sampling of twenty two ...

Studies in Computational Intelligence, 2015

Quantifying the impact of energy saving measures on a given space requires representative models ... more Quantifying the impact of energy saving measures on a given space requires representative models that can describe how energy is consumed in that space with dependence on known input variables. For this purpose, it is commonly accepted that linear regressions can be used to define those models, named energy consumption baselines. In this paper, we want to assess the performance of linear regressions to model electricity consumption compared to other modeling techniques that can capture nonlinear dynamics like fuzzy and neural networks models in three experimental places in a Portuguese University campus: a set of offices in a department, a classroom amphitheater and the library. Five input variables were defined for the study: day type, occupation, day length, solar radiation and heating and cooling degree days. The novelty of this paper is the comparative assessment between these different modeling techniques, which are usually addressed individually in the literature. From the results obtained in this research, we can outline the importance of selecting representative input variables, study their inter relation, fine tuning the models, and analyze the different models when being trained and tested. We generally conclude that neural networks have the best performance values, fuzzy models increase their performances when trained with varying epochs (with the exception of the amphitheater, where the model over fits and so as the testing performance) and linear regressions present the lowest performance. Hereupon, we discuss the encouragement of applying non-linear models such as the presented ones rather than traditionally used linear regression models, when evaluating consumption baseline to determine energy savings.