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Electrical energy saving was evaluated by taking advantage of PV and ESS in a community unit. An artificial neural network (ANN) and long short-term memory (LSTM) were employed to create a predictive model for PV generation. Annual demand data for residential buildings were estimated using EnergyPlus, while data for other buildings were collected from measurements in J Energy Town, Republic of Korea. Pearson correlation coefficients identified six crucial variables for the model. Comparative analysis of 310 cases revealed that the best-performing model was an ANN with three hidden layers and nodes of 14, 13 and 11. The model satisfied ASHRAE guidelines with a CV(RMSE) of 29.1 % and NMBE of −7.14 %. Evaluating electricity consumption in the community, case B (PV generation) showed a significant 46.3 % reduction compared to case A, while case D achieved a 5 % energy savings relative to case E over the year. [ABSTRACT FROM AUTHOR]

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency. [ABSTRACT FROM AUTHOR]

Double Skin Façade (DSF) systems have become an alternative to the environmental and energy savings issues. DSF offers thermal buffer areas that can provide benefits to the conditioned spaces in the form of improved comforts and energy savings. There are many studies conducted to resolve issues about the heat captured inside DSF. Various window control strategies and algorithms were introduced to minimize the heat gain of DSF in summer. However, the thermal condition of the DSF causes a time lag between the response time of the Heating, Ventilation, and Air-Conditioning (HVAC) system and cooling loads of zones. This results in more cooling energy supply or sometimes less than required, making the conditioned zones either too cold or warm. It is necessary to operate the HVAC system in consideration of all conditions, i.e., DSF internal conditions and indoor environment, as well as proper DSF window controls. This paper proposes an optimal air supply control for a DSF office building located in a hot and humid climate. An Artificial Neural Network (ANN)-based control was developed and tested for its effectiveness. Results show a 10.5% cooling energy reduction from the DSF building compared to the non-DSF building with the same HVAC control. Additionally, 4.5% more savings were observed when using the ANN-based control. [ABSTRACT FROM AUTHOR]

Highlights • ANN based slat angle control did not cause discomfort glare. • Optimally controlled blind slat angle using ANN showed slightly higher lighting load than the fixed slat angle cases. • As a result of the real-time optimal control of blind slat angle through the ANN, the reduction effect of the cooling load was found to be significant. • When the blind slat angle was optimally controlled through the ANN, the total load reduction effect was shown to be remarkably excellent. Abstract Windows are the only part of a building that can directly penetrate the solar radiation into the occupied space and thus the shading devices are needed to control the solar penetration. A variety of research have been conducted to develop the optimized slat angle control in the existing literature, but the research incorporating artificial intelligence technique with slat angle control is limited thus far. Therefore, in this study, the ANN (Artificial Neural Network) model was applied to minimize the combined total load consisting of lighting, cooling, and heating loads through automatic slat angle control of venetian blinds. A three-story rectangular office building was simulated using EnergyPlus, and dimming control was applied to control the lighting. The interlocked simulation between Matlab and EnergyPlus was conducted through BCVTB. As a result of comparing automatic blind control via the ANN to fixed blind slat angle, the automatic blind control via the ANN showed 9.1% lower total load than the blind angle fixed at 50°. It was confirmed that the cooling and heating load could be significantly reduced by real-time automatic control via the ANN under various operating conditions, rather than fixing the blinds at one angle. [ABSTRACT FROM AUTHOR]