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China has built four stations in Antarctica so far, and Zhongshan Station is the largest station among them. Continuous power supply for manned stations mainly relies on fuel. With the gradual increase in energy demand at the station and cost of fuel traffic from China to Zhongshan station in Antarctica, reducing fuel consumption and increasing green energy utilization are urgent problems. This research considers a standalone renewable energy system. The polar environments and renewable energy distribution of area of Zhongshan station are analyzed. The physical model, operation principle, and mathematical modeling of the proposed power system were designed. Low-temperature performance and state of charge (SOC) estimation method of the lead–acid battery were comprehensively tested and evaluated. A temperature control strategy was adopted to prevent the battery from low-temperature loss of the battery capacity. Energy management strategy of the power system was proposed by designing maximum power point tracking (MPPT) control strategies for wind turbine and PV array. The whole power system is broadly composed of a power generator (wind turbine and PV array), an uploading circuit, a three-phase rectifier bridge, an interleaved Buck circuit, a DC/DC conversion circuit, a switch circuit, a power supply circuit, an amplifier, a driver circuit, a voltage and current monitoring, a load, battery units and a control system. A case study in Antarctica was applied and can examine the technical feasibility of the proposed system. The results of the case study reveal that the scheme of standalone renewable energy system can satisfy the power demands of Zhongshan Station in normal operation. [ABSTRACT FROM AUTHOR]

Accurate short-term small-area meteorological forecasts are essential to ensure the safety of operations and equipment operations in the Antarctic interior. This study proposes a deep learning-based multi-input neural network model to address this problem. The newly proposed model is predicted by combining a stacked autoencoder and a long- and short-term memory network. The self-stacking autoencoder maximises the features and removes redundancy from the target weather station's sensor data and extracts temporal features from the sensor data using a long- and short-term memory network. The proposed new model evaluates the prediction performance and generalisation capability at four observation sites at different East Antarctic latitudes (including the Antarctic maximum and the coastal region). The performance of five deep learning networks is compared through five evaluation metrics, and the optimal form of input combination is discussed. The results show that the prediction capability of the model outperforms the other models. It provides a new method for short-term meteorological prediction in a small inland Antarctic region. [ABSTRACT FROM AUTHOR]