Building energy consumption accounts for about 40% of global primary energy use and 30% of worldwide greenhouse gas (GHG) emissions. Among the energy-related factors present in buildings, heating, cooling, and air-conditioning (HVAC) systems are considered major contributors to whole-building energy use. To improve the energy efficiency of HVAC systems and mitigate whole-building energy consumption, accurately predicting the building energy consumption can play a significant role. Although many prediction approaches are available for building energy use, a machine learning-based modeling approach (i.e., black box models) has recently been considered to be one of the most promising building energy modeling techniques due to its simplicity and flexibility compared to physics-based modeling techniques (i.e., white box models). This study presents a building energy load forecasting method based on long-term short-term memory (LSTM) and transfer learning (TL) strategies. To implement this approach, this study first conducted raw data pre-processing analysis to generate input datasets. A hospital building type was considered for a case study in the first stage. The hospital prototype building model, developed by the U.S. department of energy (DOE), was used to generate an initial input training and testing dataset for source domain tasks before the transfer learning process. For the transfer learning process in a target domain, a simulation-based analysis was also conducted to obtain target datasets by assuming limited data lengths in different weather conditions. The training and testing procedures were performed using separate cooling and heating periods with and without the transfer learning process for source and target domain tasks, respectively. Lastly, a comparative analysis was carried out to investigate how the accuracy of LSTM prediction can be enhanced with the help of transfer learning strategies. The results from this study show that the developed LSTM-TL model can achieve better performance than the prediction model, which only uses LSTM under different weather conditions. In addition, accurate performance can vary according to different transfer learning methods with frozen and fine-tuning layers and locations.

To achieve energy saving and emission reduction for vehicle diesel engines, the organic Rankine cycle (ORC) was employed to recover waste heat from vehicle diesel engines, R245fa was used as ORC working fluid, and the resulting vehicle diesel engine-ORC combined system was presented. The variation law of engine exhaust energy rate under various operating conditions was obtained, and the running performances of the screw expander were introduced. Based on thermodynamic models and theoretical calculations, the running performance of the vehicle diesel engine-ORC combined system was analyzed under various engine operating condition scenarios. Four evaluation indexes were defined: engine thermal efficiency increasing ratio (ETEIR), waste heat recovery efficiency (WHRE), brake specific fuel consumption (BSFC) of the combined system, and improvement ratio of BSFC (IRBSFC). Results showed that when the diesel engine speed is 2200 r/min and diesel engine torque is 1200 N·m, the power output of the combined system reaches its maximum of approximately 308.6 kW, which is 28.6 kW higher than that of the diesel engine. ETEIR, WHRE, and IRBSFC all reach their maxima at 10.25%, 9.90%, and 9.30%, respectively. Compared with that of the diesel engine, the BSFC of the combined system is obviously improved under various engine operating conditions.

This study analyzed the variation law of engine exhaust energy under various operating conditions to improve the thermal efficiency and fuel economy of diesel engines. An organic Rankine cycle (ORC) waste heat recovery system with internal heat exchanger (IHE) was designed to recover waste heat from the diesel engine exhaust. The zeotropic mixture R416A was used as the working fluid for the ORC. Three evaluation indexes were presented as follows: waste heat recovery efficiency (WHRE), engine thermal efficiency increasing ratio (ETEIR), and output energy density of working fluid (OEDWF). In terms of various operating conditions of the diesel engine, this study investigated the variation tendencies of the running performances of the ORC waste heat recovery system and the effects of the degree of superheat on the running performance of the ORC waste heat recovery system through theoretical calculations. The research findings showed that the net power output, WHRE, and ETEIR of the ORC waste heat recovery system reach their maxima when the degree of superheat is 40 K, engine speed is 2200 r/min, and engine torque is 1200 N·m. OEDWF gradually increases with the increase in the degree of superheat, which indicates that the required mass flow rate of R416A decreases for a certain net power output, thereby significantly decreasing the risk of environmental pollution.