Calibrating whole building energy model: a case study using BEMS data

This paper describes a Calibration methodology which is specifically configured to best match actual building performance, based on a case study conducted to calibrate whole building energy model using Building Energy Management System (BEMS) measured data. It details the calibration approach which was designed to meet the specific characteristic of the spaces, systems and energy use in the pilot school building. Two calibration methods were developed; one is for electrical and the other is for thermal energy along with calibrated weather file. The result shows excellent correlation with the measured electricity and room air temperature and demonstrates the effectiveness of the methodology. Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error (CVRMSE) for electricity consumption is 6% and 14% respectively and -5 and 7% for air temperature

Предвиђање потрошње КГХ система применом метода вештачке интелигенције

С обзиром да је сектор зградарства у Европи одговоран за 40% укупне потрошње енергије, као и за 36% укупне емисије СО2, енергертска ефикасност, а самим тим и анализа потрошње енергије су теме од великог значаја...Due to the fact that in Europe buildings account for 40% of total energy use and 36% of total CO2 emission estimation or prediction of building energy consumption is lately topic of greatest interest. This research filed involves various scientific domains. The main idea of this dissertation is to investigate application of artificial intelligence in building energy use prediction. In the statistical (data-driven) approach it is required that the input and output variables are known and measured, and the development of the “black box” model consists in determination of a mathematical description of the relationship between the independent variables and the dependent one..

Предвиђање потрошње КГХ система применом метода вештачке интелигенције

С обзиром да је сектор зградарства у Европи одговоран за 40% укупне потрошње енергије, као и за 36% укупне емисије СО2, енергертска ефикасност, а самим тим и анализа потрошње енергије су теме од великог значаја...Due to the fact that in Europe buildings account for 40% of total energy use and 36% of total CO2 emission estimation or prediction of building energy consumption is lately topic of greatest interest. This research filed involves various scientific domains. The main idea of this dissertation is to investigate application of artificial intelligence in building energy use prediction. In the statistical (data-driven) approach it is required that the input and output variables are known and measured, and the development of the “black box” model consists in determination of a mathematical description of the relationship between the independent variables and the dependent one..

COMPARATIVE ANALYSIS OF ERP SYSTEMS: MICROSOFT DYNAMICS NAV AND ODOO COMMUNITY

<p><i><strong>Abstract</strong></i></p><p><i>The paper provides a detailed comparison between two widely used ERP systems, Microsoft Dynamics NAV 2017 and Odoo Community, focusing on their performance within a Project Management scenario. The goal of the research is to evaluate and contrast the functionalities, scalability, and customization capabilities of each system, particularly in relation to their suitability for project and service management. The paper emphasizes the significance of thoroughly assessing organizational requirements before selecting an appropriate ERP system, highlighting the crucial role of tailored decision-making in this process.</i></p><p><strong>References:</strong></p><ol><li>Bradford, M. (2014). <i>Modern ERP: select, implement, and use today's advanced business systems</i>. Lulu. com.</li><li><i>Chapter 1: Architecture Overview — Odoo 16.0 documentation</i>. (n.d.). Retrieved September 4, 2023, from <a href="https://www.odoo.com/documentation/16.0/developer/tutorials/getting_started/01_architecture.html">https://www.odoo.com/documentation/16.0/developer/tutorials/getting_started/01_architecture.html</a></li><li>Ganesh, A., Shanil, K. N., Sunitha, C., & Midhundas, A. M. (2016, February). Openerp/odoo-an open source concept to erp solution. In <i>2016 IEEE 6th International Conference on Advanced Computing (IACC)</i> (pp. 112-116). IEEE.</li><li>Jindal, N., & Dhindsa, K. S. (2013). Comparative Study of OpenERP and its Technologies. <i>International Journal of Computer Applications</i>, <i>73</i>(20), 42-47.</li><li>Jswymer. (2018, January 2). <i>Product Architecture - Dynamics NAV</i>. Microsoft Learn. Retrieved September 4, 2023, from <a href="https://learn.microsoft.com/en-us/dynamics-nav/product-and-architecture-overview">https://learn.microsoft.com/en-us/dynamics-nav/product-and-architecture-overview</a></li><li>Jswymer. (2022a, May 3). <i>Personalizing pages in the Dynamics Windows Client - Dynamics NAV app</i>. Microsoft Learn. Retrieved September 4, 2023, from <a href="https://learn.microsoft.com/en-us/dynamics-nav-app/ui-personalization-windows-client">https://learn.microsoft.com/en-us/dynamics-nav-app/ui-personalization-windows-client</a></li><li>Jswymer. (2022b, June 9). <i>Client Types - Dynamics NAV</i>. Microsoft Learn. Retrieved September 4, 2023, from <a href="https://learn.microsoft.com/en-us/dynamics-nav/client-types">https://learn.microsoft.com/en-us/dynamics-nav/client-types</a></li><li>Jswymer. (2022c, June 9). <i>SQL Server Database Components - Dynamics NAV</i>. Microsoft Learn. Retrieved September 4, 2023, from <a href="https://learn.microsoft.com/en-us/dynamics-nav/sql-server-database-components">https://learn.microsoft.com/en-us/dynamics-nav/sql-server-database-components</a></li><li>Leon, A. (2014). <i>Enterprise resource planning</i>. McGraw-Hill Education (India) Pte Limited.</li></ol&gt

Eliksir F1 - The new sweet corn hybrid (Zea mays Var. Sacharata Sturt.)

In order to create Eliksir F1 a new hybrid, we performed a crossing of original sweet corn lines which were obtained from an artificial population by employing the inbreeding method. The aim of this paper is to compare the most significant characteristics of this hybrid and the following American hybrids: Sundance F1 Early Arctic F1, Spring Gold F1 and Reliance F1. The characteristics taken into consideration were: the earliness, total yield and the grain cob ratio. The experiment was carried out by using the random block system in four replications. The hybrids' differences were tested by Fisher's variance analysis. We found that Eliksir F1 was an early hybrid of sweet corn with the average of 1.3 ears and 1.7 side branches per plant. Eliksir F1 had the highest yield comparing to the other hybrids. The yield ranged from 11,955 kg/ha. The grain percentage was 62% of the total yield. Therefore, Eliksir F1 could be recommended as suitable for sweet corn hybrid early production

Multistage ensemble of feedforward neural networks for prediction of heating energy consumption

Feedforward neural network models are created for prediction of heating energy consumption of a university campus. Actual measured data are used for training and testing the models. Multistage neural network ensemble is proposed for the possible improvement of prediction accuracy. Previously trained feed-forward neural networks are first separated into clusters, using k-means algorithm, and then the best network of each cluster is chosen as a member of the ensemble. Three different averaging methods (simple, weighted and median) for obtaining ensemble output are applied. Besides this conventional approach, single radial basis neural network in the second level is used to aggregate the selected ensemble members. It is shown that heating energy consumption can be predicted with better accuracy by using ensemble of neural networks than using the best trained single neural network, while the best results are achieved with multistage ensemble