Solar power is the most widely used renewable energy source, which reduces pollution consequences from using conventional fossil fuels. However, supplying stable power from solar power generation remains challenging because it is difficult to forecast power generation. Accurate prediction of solar power generation would allow effective control of the amount of electricity stored in batteries, leading in a stable supply of electricity. Although the number of power plants is increasing, building a solar power prediction model for a newly constructed power plant usually requires collecting a new training dataset for the new power plant, which takes time to collect a sufficient amount of data. This paper aims to develop a highly accurate solar power prediction model for multiple power plants available for both new and existing power plants. The proposed method trains the model on existing multiple power plants to generate a general prediction model, and then uses it for a new power plant while waiting for the data to be collected. In addition, the proposed method tunes the general prediction model on the newly collected dataset and improves the accuracy for the new power plant. We evaluated the proposed method on 55 power plants in Japan with the dataset collected for two and a half years. As a result, the pre-trained models of our proposed method significantly reduces the average RMSE of the baseline method by 73.19%. This indicates that the model can generalize over multiple power plants, and training using datasets from other power plants is effective in reducing the RMSE. Fine-tuning the pre-trained model further reduces the RMSE by 8.12%.

Because solar energy is intermittent and a ship's power-system load fluctuates and changes abruptly, in this work, the solar radiation parameters were adjusted according to the latitude and longitude of the ship and the change of the sea environment. An objective function was constructed that accounted for the cost and service life simultaneously to optimize the configuration of the marine diesel engine hybrid energy system. Finally, the improved artificial bee colony algorithm was used to optimize and obtain the optimal system configuration. The feasibility of the method was verified by ship navigation tests. This method exhibited better configuration performance optimization than the traditional methods.

A vibration-to-electric energy converter as a power generator through a variable-resonating capacitor is theoretically and experimentally demonstrated as a potential on-chip battery. The converter is constructed from three components: a mechanical-variable capacitor, a charge-transporter circuit and a timing-capture control circuit. An optimum design methodology is theoretically described to maximize the efficiency of the vibration-to-electric energy conversion. The energy-conversion efficiency is analyzed based on the following three factors: the mechanical-energy to electric-energy conversion loss, the parasitic elements loss in the charge-transporter circuit and the timing error in the timing-capture circuit. Through the mechanical-energy conversion analysis, the optimum condition for the resonance is found. The parasitic elements in the charge-transporter circuit and the timing management of the capture circuit dominate the output energy efficiency. These analyses enable the optimum design of the energy-conversion system. The converter is fabricated experimentally. The practical measured power is 0.12 µW, and the conversion efficiency is 21%. This efficiency is calculated from a 43% mechanical-energy conversion loss and a 63% charge-transportation loss. The timing-capture circuit is manually controlled in this experiment, so that the timing error is not considered in the efficiency. From our result, a new system LSI application with an embedded power source can be explored for the ubiquitous computing world.