Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R²), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R², 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value.

Axial flow fans are commonly used for a system or machinery cooling process. It also used for ventilating warehouses, factories, and garages. In the fan manufacturing industry, the demand for varying fan operating points makes design parameters complicated because many design parameters affect the fan performance. This study combines the deep neural network (DNN) with a genetic algorithm (GA) for axial flow design and development. The characteristic fan curve (P-Q Curve) can be generated when the relevant fan parameters are imported into this system. The system parameters can be adjusted to achieve the required characteristic curve. After the wind tunnel test is performed for verification, the data are integrated and corrected to reduce manufacturing costs and design time. This study discusses a small axial flow fan NACA and analyzes fan features, such as the blade root chord length, blade tip chord length, pitch angle, twist angle, fan diameter, and blade number. Afterwards, the wind tunnel performance test was performed and the fan performance curve obtained. The feature and performance test data were discussed using deep learning. The Python programming language was used for programming and the data were trained repeatedly. The greater the number of parameter data, the more accurate the prediction. Whether the performance condition is met could be learnt from the training result. All parameters were calculated using a genetic algorithm. The optimized fan features and performance were screened out to implement the intelligent fan design. This method can solve many fan suppliers’ fan design problems.

In this study, silica gel and sodium polyacrylate desiccants are coated onto a finned tube heat exchanger (Desiccant Coating Heat Exchanger, DCHE), which can absorb the vapor in the process air for dehumidification. In the experiments, the desiccant is coated on fins using the dense coating method, which causes the fixed fin area to be coated with greater amounts of desiccants for a better dehumidification performance. This study discusses the dehumidification performances of a single stage DCHE and two-stage DCHEs in series under different relative humidity conditions of the inlet process air and different regeneration water temperatures. The results show that the thermal coefficient of performance (COP_th) of the DCHEs for the two desiccants prepared by the dense coating method is better than that of DCHEs with the general immersing coating method by a factor of 2–2.4. The two-stage DCHEs in series have a lower supply humidity ratio than a single stage DCHE at different inlet humidity levels, and they can be used in the industry when a special low humidity manufacturing process is required. The overall dehumidifying capacities of two-stage series-connected DCHEs at regeneration temperatures of 50 °C and 70 °C are approximately twice as high as those of a single stage DCHE. The COP_th value of a single stage or two stages increases with an increase in the inlet humidity of the process air. The COP_th values of the sodium polyacrylate single stage and two-stage DCHEs are 1–1.3 times greater than those of the silica gel single stage and two-stage DCHEs at a high inlet air humidity. Finally, the effects of different regeneration water temperatures on the performance of DCHEs are investigated. With an increase in the regeneration water temperature, the COP_th value, dehumidifying capacity and regeneration capacity of single stage or two-stage DCHEs increase as well.