Abstract
In this article a simple computer tool that can predict energy demand of residential buildings area in Poland by means of artificial neural network is developed and its application is demonstrated. Authors focused on energy demand analyses for single-family houses, representative for the Polish household sector. Advanced computer simulations were performed by means of the Energy Plus software, with hourly calculation step. Then, the obtained results and simulation parameters were used as input data for artificial neural network analysis. As a result, authors developed a simple, user friendly computer tool that can predict, with relatively good accuracy in comparison to the Energy Plus results, energy demand for residential buildings located in Poland. The software might be used for local (a single building) or regional (whole areas, neighbourhoods) analyses of single-family houses in Polish household sector. Additionally, for the analysed area, Renewable Energy potential can be checked – the developed software allows for analyses of solar energy application in the building/neighbour-hood design. Some examples of the energy analyses performed by means of the developed software have been presented.
Highlights
Energy efficiency is one of the most important factors according to global strategies concerning sustainability
For daily period the maximum difference was equal to 28%, while annual heating demand value differences fluctuated between 1% and 9%
For purposes of the performed Energy Cluster analysis this is a sufficient accuracy of a single building energy consumption prediction – the main effort is focused at analysing energy efficiency increase globally
Summary
Energy efficiency is one of the most important factors according to global strategies concerning sustainability. In Poland, household sector consists mainly of single-family houses, which in most of the cases require thermomodernisation to improve their energy efficiency. The article focuses on energy demand analysis for a neighbourhood consisting of singlefamily houses representative for Poland. Https://doi.org/10.10 51/matecconf /201928202072 able to predict energy profile and to map energy characteristics of the analysed area. The software uses artificial neural network (ANN) to predict energy demand profile of an analysed single building or considered area (e.g. energy cluster). The built-in database consists of results from the Energy Plus simulations and might be expanded anytime. Some analyses of solar energy application can be performed using the developed software as well
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