index.Rmd

--- 
title: "ENCOME - Energy Conservation Measure Tool"
author: "Daniele Antonucci, Ulrich Filippi Oberegger, Olga Somova, Mattia Dallapiccola"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
documentclass: book
bibliography: [book.bib, packages.bib]
# url: your book url like https://bookdown.org/yihui/bookdown
# cover-image: path to the social sharing image like images/cover.jpg
description: |
  This is a minimal example of using the bookdown package to write a book.
  The HTML output format for this example is bookdown::gitbook,
  set in the _output.yml file.
link-citations: yes
github-repo: https://danieleantonucci20.github.io/ENCOME_guidelines/


---





# About

Increasing the energy efficiency of existing buildings is a priority from three different points of view: the environment, the security of energy supply and the quality of life of citizens. The renovation of existing buildings has a particularly high potential. It is therefore logical, from an economic and operational point of view, to implement appropriate energy saving measures when a building is renovated. The ENergy COnservation MEasure (ENCOME) tool developed by Eurac in different projects (MATRYCS, MODERATE, INFINITE) aims to evaluate the best trad-off between reduction of energy consumption of building and improving comfort, cost of interventions and de-risking in energy efficiency.


## How it works 

```{r GeneralSchema, echo=FALSE, fig.cap="Flow-chart of ENCOME tool", out.width = '90%'}
knitr::include_graphics("image/GeneralSchema.png")
```

The tool provides: 

1. **BUILDING EMERGY SIMULATION**
 
 - the (sensible) energy need for heating and cooling, based on hourly or monthly calculations
 - the internal temperature, based on hourly calculations;
 - the sensible heating and cooling load, based on hourly calculations;
 
 trough the implementation of the ISO-52016:2017 calculation.
 The calculation methods can be used for residential or non-residential buildings, or a part of it, referred to as "the building" or the "assessed object".
 
2. **BUILDING ENERGY OPTIMIZATION (ongoing)**
One of the main issue related to building energy simulation is the reduction of the gap between real data and simulated data

The goal consists in the development of a calibration approach to reduce the differences between building simulation and real monitored data (performance gap) using a Particle Swarm Optimization tool and Genetic algorithm in dynamic building simulation. 
The tools works on some specific factor that affects the calculation of the energy consumption of the building such as the internal radiative and convective heat transfer coefficients.

3. **ENERGY CONSERVATION MEASUREs**

Based on the simulated and calibrated data, the tool provides the possibility to reduce the energy consumption of the building selecting different ECMs related to facade and energy systems.

4. **PV SIMULATION AND OPTIMIZATION TOOL**
....


```{r include=FALSE}
# automatically create a bib database for R packages
knitr::write_bib(c(
  .packages(), 'bookdown', 'knitr', 'rmarkdown'
), 'packages.bib')
```

```{js, echo = FALSE}
title=document.getElementById('header');
title.innerHTML = '<img src="image/ENCOME.png" alt="Test Image">' + title.innerHTML
```