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The study uses four AC technologies to assess the energy performance—this is a case study of an educational building in Barranquilla, Colombia. The building currently has split AC technology high-energy consumers. Therefore, it was necessary to assess a replacement with more efficient technology. Because of the non-seasonal climate in the building location, one month of monitoring of energy consumption was the reference for developing an energy model for the building using EnergyPlus and DesignBuilder software. The model was applied to forecast the building energy performance of our more efficient AC technologies available in the Colombian market, and valuable according to building specifications (Split, VRF, VAV, and Chiller). Results show a reduction in energy consumption of approximately 30% with the technology change and 15% savings in life cycle costs (LCCs), even though the building is already considered to have a low energy consumption according to national regulations. The findings of this study underscore the potential for widespread applicability across all types of buildings, regardless of their energy consumption profile, be it low, medium, or high. This extensive applicability not only highlights the adaptability and versatility of the technology but also underscores its significance in achieving substantial energy savings and cost reductions across the entire building industry, contributing to a more sustainable and economically efficient future. [ABSTRACT FROM AUTHOR]

The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and energy optimization strategies in the Heating, Ventilation, and Air Conditioning (HVAC) systems. To implement a successful energy management strategy in a building, a data-driven approach should accurately forecast the HVAC features, in particular the indoor temperatures. Accurate predictions not only increase thermal comfort levels, but also play a crucial role in saving energy consumption. This study aims to investigate the capabilities of data-driven approaches and the development of a model for predicting indoor temperatures. A case study of an educational building is considered to forecast indoor temperatures using machine learning and deep learning algorithms. The algorithms' performance is evaluated and compared. The important model parameters are sorted out before choosing the best architecture. Considering real data, prediction models are created for indoor temperatures. The results reveal that all the investigated models are successful in predicting indoor temperatures. Hence, the proposed deep neural network model obtained the highest accuracy with an average RMSE of 0.16 °C, which renders it the best candidate for the development of a digital twin. [ABSTRACT FROM AUTHOR]

It has been challenging for designers to identify the appropriate design parameters that would reduce building energy consumption while achieving thermal comfort for building occupants. This study aims to determine the most important architectural building design parameters (ABDPs) that can increase thermal comfort and reduce energy use in educational buildings. The effect of 15 ABDPs in an Australian educational lecture theatre and their variabilities on energy consumption and students' thermal comfort for each parameter were analysed using Monte Carlo (MC) techniques. Two thousand simulations for every input parameter were performed based on the selected distribution using the Latin hypercube sampling (LHS) technique. Sensitivity analyses (SA) and uncertainty analyses (UA) were used to assess the most important ABDPs in terms of thermal discomfort hours and energy consumption. The study found that the ABDPs, such as cooling set-point temperatures and roof construction, significantly reduce the operative temperature by up to 14.2% and 20.0%, respectively. Consequently, these reductions could significantly shorten the thermal discomfort hours, thereby reducing energy consumption by 43.7% and 41.0%, respectively. The findings of this study enable building designers to identify which ABDPs have a substantial impact on thermal comfort and energy consumption. [ABSTRACT FROM AUTHOR]