Abstract
This paper aims at presenting the development of a calibrated building energy simulation model of a school building to study the impact of improving the ventilation system on energy performance. The simulation model was developed with the DesignBudderlEnergyplus software and it was calibrated based on data collected during an energy audit to the school building. Schools need high outdoor airflow rates to remove indoor air contaminants related to occupants and building components, thus requiring mechanical ventilation systems. Due to budget restrictions, school managers decided to schedule the building management system to keep the HVAC systems active only between 6:00 am and 10:00 am. According to the values measured in this school, it was patent that the CO2 concentration was too high in certain periods. Too high peak values undermine the indoor air quality in the remaining occupancy time of the classroom, harming the work conditions for teachers and students. To solve this problem, an extended usage schedule of the mechanical ventilation was simulated (8:00 am to 5:00 pm) according to the required enhancement of indoor air quality, which together with the adoption of the new calculated fresh air flow rates will enhance air quality while avoiding excessive cost, thus increasing energy efficiency. (C) 2017 The Authors. Published by Elsevier Ltd.

Abstract

Abstract
This paper presents results of work developed in the field of building energy benchmarking applied to the building stock of the Polytechnic Institute of Leiria, Portugal, based on a thorough energy performance characterisation of each of its buildings. To address the benchmarking of the case study buildings, an energy efficiency ranking system was applied. Following an energy audit of each building, they were grouped in different typologies according to the main end-use activities developed: Pedagogic buildings, canteens, residential buildings and office buildings. Then, an energy usage indicator was used to establish a metric to rank the buildings of each typology according to their energy efficiency. The energy savings potential was also estimated, based on the reference building energy usage indicator for each typology, and considering two different scenarios, yielding potential savings between 10% and 34% in final energy consumption.

Abstract
Forecasting heating and cooling energy demand in buildings plays a critical role in supporting building management and operation. Thus, analysing the energy consumption pattern of a building could help in the design of potential energy savings and also in operation fault detection, while contributing to provide proper indoor environmental conditions to the building's occupants.This paper aims at presenting the main results of a study consisting in forecasting the hourly heating and cooling demand of an office building located in Lisbon, Portugal, using machine learning models and analysing the influence of exogenous variables on those predictions. In order to forecast the heating and cooling demand of the considered building, some traditional models, such as linear and polynomial regression, were considered, as well as artificial neural networks and support vector regression, oriented to machine learning. The input parameters considered in the development of those models were the hourly heating and cooling energy historical records, the occupancy, solar gains through glazing and the outside dry-bulb temperature.The models developed were validated using the mean absolute error (MAE) and the root mean squared error (RMSE), used to compare the values obtained from machine learning models with data obtained through a building energy simulation performed on an adequately calibrated model.The proposed exploratory analysis is integrated in a research project focused on applying machine learning methodologies to support energy forecasting in buildings. Hence, the research line proposed in this article corresponds to a preliminary project task associated with feature selection/extraction and evaluation of potential use of machine learning methods. © 2020 IEEE.

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.