Despite increasing energy efficiency requirements, the full potential of energy efficiency is still unlocked; many buildings in the EU tend to consume more energy than predicted. Gathering data and developing models to predict occupants’ behaviour is seen as the next frontier in sustainable design. Measurements in the analysed open-space office showed accordingly 3.5 and 2.7 times lower occupancy compared to the ones given by DesignBuilder’s and EN 16798-1. This proves that proposed occupancy patterns are only suitable for typical open-space offices. The results of the previous studies and proposed occupancy prediction models have limited applications and limited accuracies. In this paper, the hybrid differential evolution online sequential extreme learning machine (DE-OSELM) model was applied for building occupants’ presence prediction in open-space office. The model was not previously applied in this area of research. It was found that prediction using experimentally gained indoor and outdoor parameters for the whole analysed period resulted in a correlation coefficient R2 = 0.72. The best correlation was found with indoor CO2 concentration—R2 = 0.71 for the analysed period. It was concluded that a 4 week measurement period was sufficient for the prediction of the building’s occupancy and that DE-OSELM is a fast and reliable model suitable for this purpose.

Implementing provisions of the EPBD all Member States require to provide EPC (Energy Performance Certificate) when buildings are c onstructed, sold or rented. The purpose of the certificate is to compare buildings’ performance and inform the end-users. However, quite many mismatches and discrepancies could be found when comparing actual energy consumption with the once declared by the EPC. This mismatch of energy demand is known as Energy Performance Gap (EPG). It was analysed by different researchers on national levels. In the study, an overall overview of the high-performance buildings in Lithuania is performed and EPG is analysed using statistical indicators. Analysis has shown that for class A the EPG varies from −101 % to +77 %. More buildings are found to have a positive Energy Performance Gap. For class A+ and A++ variations are within a narrower interval: from +18 to 76 % and from +23 to 77 % accordingly. It confirms the findings in the other countries that very high-energy performance buildings tend to consume more than predicted. Also it is confirmed that despite differences in national certification methodologies, the same problem (just of different scale) exists and EPC schemes need revisions.

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Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO2 concentrations. Both models show similar and high accuracy of prediction: R2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.

Recently photovoltaic (PV) is widely used technology in modern buildings as part of the façade. Building integrated photovoltaic (BIPV) might be transparent or opaque, mounted on the envelope or might be a construction element of the envelope-a window. Energy production and efficiency of 3 different BIPV systems is simulated and economical assessment of the projects is performed. It shows that attractive solution when energy is consumed for own purposes is opaque PV system and also one of the cases with PV window system. PV window is transmitting light into the space at the same time producing electricity and performing as shading of the window. Therefore PV window should be assessed taking into account more criteria than just electricity generated. The effect of PV shading on heating, cooling and lighting energy has to be taken into account. The paper presents analysis of overall performance of PV window based on the simulations and measurements. Results show that even though electricity generation of the window is relatively small, in summer it works as an efficient sun shading thus giving a potential for the reduction of investments for cooling equipment and savings on cooling energy demand.