The collaboration development between the power supply, solar power, and storage batteries is a formidable commission in the smart solar microgrid. The efficient power management integrated into a smart solar microgrid is required to address these problems. The deep learning predictive tools are utilized to forecast the maximum building’s three-phase load power in the short term. In this project, historical data, from the solar module, battery module, grid data, and climate data, are collected by integrated advanced metering infrastructure in a smart solar microgrid in Taiwan. These historical data are utilized in predicting the building’s three-phase load power in the smart solar microgrid. This project proposed the hybrid model in the short-term three-phase loads forecasting based on convolution neural network bidirectional gated recurrent unit (CNN-Bi-GRU). The CNN-Bi-GRU utilizes the continuous-time sliding window, which is extracted features and reshaped into vectors by CNN layers. The hyperparameter optimization is utilized to construct the highest performance structure of the CNN-Bi-GRU model. The CNN-Bi-GRU performance is compared and evaluated with other state-of-arts models, which are the recurrent neural network (RNN), LSTM, gated recurrent unit (GRU), bidirectional LSTM (Bi-LSTM), and Bi-GRU models. The experiment results prove that the forecasting accuracy could be effectively improved by a hybrid CNN-Bi-GRU model with an appropriate window size of sequential historical data. According to research knowledge, this is the first work to predict the building’s three-phase load power using multiple source data from advanced metering infrastructures (AMI) and deep learning CNN-Bi-GRU in the smart solar microgrid. The CNN-Bi-GRU model is successfully integrated into the efficient power management, which maintains the adequate battery storage level, solar power, and grid power balance under different weather conditions and working requirements of the building’s three-phase load power.

This paper presents an online monitoring system to classify the leakage/discharge current of the insulator in coastal sites using Bidirectional Long short-term memory (Bi-LSTM) models on a web-based service. The remote monitoring methodology uses environmental parameters to classify the peak level of leakage/discharge current of the 15kV and 25kV distribution insulators. The sequential weather data, the humidity, temperature, rainfall, dew point, solar illumination, wind speed, air pressure, and wind direction are automatically collected hourly in real-time and transferred to data servers. The hyperparameter optimization for the structure of Bi-LSTM is utilized through the grid search capability in a deep learning machine. The optimized design of Bi-LSTMs improves the performance and accuracy in predicting the leakage/discharge current classification for HDPE and SR of 15kV and 25kV insulators. Compared with persistent methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM), and the gated recurrent unit (GRU), the Bi-LSTM has better performance and higher accuracy in predicting the leakage/discharge levels, which are utilized to evaluate the surface pollution of insulators. The optimized structure of the proposed Bi-LSTM model could achieve a maximum improvement of 49.529% error, 12.761% accuracy, 72.736% error, and 36.641% accuracy for training and validating data compared with other models. Moreover, web-based service is developed for maintenance staff to interact with all current and predicted status of insulators. This online leakage current classification has been installed in Mailiao Township in Taiwan and could establish a reasonable maintenance mechanism for 15kV and 25kV distribution insulators.

The tourism industry experienced a positive increase after COVID-19 and is the largest segment in the foreign exchange contribution in developing countries, especially in Vietnam, where China has begun reopening its borders and lifted the pandemic limitation on foreign travel. This research proposes a hybrid algorithm, combined convolution neural network (CNN) and long short-term memory (LSTM), to accurately predict the tourism demand in Vietnam and some provinces. The number of new COVID-19 cases worldwide and in Vietnam is considered a promising feature in predicting algorithms, which is novel in this research. The Pearson matrix, which evaluates the correlation between selected features and target variables, is computed to select the most appropriate input parameters. The architecture of the hybrid CNN–LSTM is optimized by utilizing hyperparameter fine-tuning, which improves the prediction accuracy and efficiency of the proposed algorithm. Moreover, the proposed CNN–LSTM outperformed other traditional approaches, including the backpropagation neural network (BPNN), CNN, recurrent neural network (RNN), gated recurrent unit (GRU), and LSTM algorithms, by deploying the K-fold cross-validation methodology. The developed algorithm could be utilized as the baseline strategy for resource planning, which could efficiently maximize and deeply utilize the available resource in Vietnam.