Abstract
Nowadays, as more data is now available from an increasing number of installed sensors, load forecasting applied to buildings is being increasingly explored. The amount and quality of resulting information can provide inputs for smarter decisions when managing and operating office buildings. In this article, the authors use two data-driven methods (artificial neural networks and support vector machines) to predict the heating and cooling energy demand in an office building located in Lisbon, Portugal. In the present case-study, these methods prove to be an accurate and appealing alternative to the use of accurate but time-consuming multi-zone dynamic simulation tools, which strongly depend on several parameters to be inserted and user expertise to calibrate the model. Artificial neural networks and support vector machines were developed and parametrized using historical data and different sets of exogenous variables to encounter the best performance combinations for both the heating and cooling periods of a year. In the case of support vector regression, a variation introduced simulated annealing to guide the search for different combinations of hyperparameters. After a feature selection stage for each individual method, the results for the different methods were compared, based on error metrics and distributions. The outputs of the study include the most suitable methodology for each season, and also the features (historical load records, but also exogenous features such as outdoor temperature, relative humidity or occupancy profile) that led to the most accurate models. Results clearly show there is a potential for faster, yet accurate machine-learning based forecasting methods to replace well-established, very accurate but time-consuming multi-zone dynamic simulation tools to forecast building energy consumption.

Abstract
A major rehabilitation programme of secondary school buildings has been carried out in the last few years in Portugal. With the introduction of HVAC systems in buildings that were previously naturally ventilated, an increase on energy consumption has been verified. During the first occupancy periods of new and refurbished buildings, energy and indoor climate quality audits are important strategies to improve the buildings’ energy use. In this context, this paper aims at showing the relations between the energy consumption and indoor environment quality (IEQ) parameters, obtained from the energy and IEQ audit in six representative modernised secondary schools – part of a larger R&D project untitled 3Es – geographically and climatically distributed in Portugal mainland. The monitoring period during the mid-season 2013 varied between schools, between two and three weeks. Air exchange rates, more specifically infiltration rates, were quantified aiming at determining the current airtightness condition of the refurbished schools. A subjective IEQ assessment was also performed, focusing on occupants' feedback, providing insight on the potential linkages between energy use and occupants’ comfort. A reflection on the energy consumption indicators and the indoor conditions obtained in the classrooms was proposed, and some suggestions were anticipated.

Abstract
There is a large consensus on the importance of human behaviour on the energy performance of buildings. Energy policy orientations consistently take into account the importance of the building stock in the overall energy use by societies. Existing buildings are specially challenging, as reliable diagnoses are required to take appropriate action towards comfort and energy efficiency. Managers also play a determinant role in building performance, either on investment decisions in technology or on the building operation. Middle-out agents may determine effective energy use options and positively influence behaviours at both levels, top management and occupants. Interdisciplinary cooperation amongst members of research teams is required for energy-efficient building design, coping simultaneously with technological and behavioural issues and their interrelations, privileging occupant-centred approaches.

Abstract
Several technological, social and organizational factors influence energy management in school buildings, resulting in a complex situation away from the usual engineering approach. The selection of evaluation criteria to assess the energy performance of school buildings remains one of the most challenging aspects since these should accommodate the perspectives of the potential key stakeholders. This paper presents a comprehensive problem structuring approach combining Soft Systems Methodology and Value Focused Thinking to elicit and organize the multiple aspects that influence energy efficiency of school buildings. The main aim of this work is structuring the fundamental objectives to develop a criteria tree to be considered in a multi-criteria classification model to be used by management entities for rating overall energy performance of school buildings. This methodological framework helped grasping the main issues at stake for a thorough energy performance assessment of school buildings and the need to define adequate policies for improvement.

Abstract
As the access to consumption data available in household smart meters is now very common in several developed countries, this kind of information is assuming a providential role for different players in the energy sector. The proposed study was applied to data available from the Smart Meter Energy Consumption Data in the London Households dataset, provided by UK Power Networks, containing half-hourly readings from an original sample of 5567 households (71 households were hereby carefully selected after a justified filtering process). The main aim is to forecast the day-ahead load profile, based only on previous load values and some auxiliary variables. During this research different forecasting models are applied, tested and compared to allow comprehensive analyses integrating forecasting accuracy, processing times and the interpretation of the most influential features in each case. The selected models are based on Multivariate Adaptive Regression Splines, Random Forests and Artificial Neural Networks, and the accuracies resulted from each model are compared and confronted with a baseline (Naive model). The different forecasting approaches being evaluated have been revealed to be effective, ensuring a mean reduction of 15% in Mean Absolute Error when compared to the baseline. Artificial Neural Networks proved to be the most accurate model for a major part of the residential consumers.

Abstract
During the next decades the refurbishment of old buildings will be an essential way to contribute to the global improvement of buildings energy performance indicators. Within this context, the present paper is focused on the use of electrochromic (EC) windows, an emerging technology alternative to shading devices, to control solar gains in buildings located in Mediterranean climates. The optical properties adjustments of the EC glasses are discussed based on the incident solar radiation. The ESP-r building energy simulation software was used to study the energy savings resulting from the application of electrochromic windows, considering the comparison of several windows solutions (single, double-glazing and EC windows) and windows orientations (East, South and West). In addition, different transition ranges for the optical properties of the EC glasses are assessed through the analysis of the energy needs for space heating and cooling. The main conclusion is that EC technology is an effective option in cooling dominated buildings. The impact of EC windows is highly dependent on facade orientation, being a valid option particularly in the cases of the East and West facades. For these facades, the control set point found to be effective corresponds to an incident solar radiation on the glass of 150 W/m(2) to impose a total coloured state. For the South facade the results show no significant advantage of using EC windows.