Abstract
Over the last few years, mechanical biological treatment systems for municipal solid waste have been introduced in many European countries. In most cases, this was driven by the European Union Landfill Directive, which requires the diversion of biodegradable municipal waste from landfill to alternative processes. Although this type of treatment allows energy recovery from municipal solid waste, the process of mechanical biological treatment appears to be an intensive energy consumer, due to high demand of electricity consumed by process equipment. This paper presents the main results of an energy audit performed to a Mechanical Biological Treatment facility in Portugal, which due to the amount of energy consumed must comply with the Portuguese Program called Intensive Energy Consumption Management System – SGCIE. The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. To implement actions that improve energy efficiency, it is necessary for the facilities operation to be associated with an effective energy management methodology, as well as an efficient facilities management procedure. The implementation of any energy management system should start with an energy audit, which was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns and sector/equipment load profiles. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. Results shown that there is a considerable potential for reducing energy consumption and greenhouse gases emissions on Mechanical Biological Treatment units. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.

Abstract
This paper aims at presenting the main results of an energy audit performed to a gypsum production plant, in Portugal, which due to the amount of energy consumed must comply with the Portuguese program SGCIE (Intensive Energy Consumption Management System). The program was created in 2008 to promote energy efficiency and energy consumption monitoring in intensive energy consuming facilities (energy consumption higher than 500 toe per year). Facilities operators are required to perform energy audits and take actions to draw up an action plan for energy efficiency, establishing targets for energy consumption reduction and greenhouse gases emissions indexes. An energy audit was carried out to identify potential energy conservation measures for improving energy efficiency, and also typical energy consumption patterns, sector/equipment load profiles and thermal equipment performance. This tool gives managers the information to support decision making on improving energy performance and reducing greenhouse gas emissions. A number of tangible targets and measures were devised and set to be implemented in the next few years. Results show that there is a considerable potential for reduction in the energy consumption and greenhouse gases emissions of gypsum manufacturing plants. Here, as elsewhere in the industrial sector, energy efficiency can only be achieved through a continuous energy monitoring and management system.

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.