High efficiency power feeding systems are effective solutions for reducing the ICT power consumption with reducing power consumption of the ICT equipment and cooling systems. A higher voltage direct current (HVDC) power feeding system prototype was produced. This system is composed of a rectifier equipment, power distribution unit, batteries, and the ICT equipment. The configuration is similar to a -48 V DC power supply system. The output of the rectifier equipment is 100 kW, and the output voltage is 401.4 V. This paper present the configuration of the HVDC power feeding system and discuss its basic characteristics in the prototype system.

A fuel cell energy system is under development for supply of generated electrical energy to telecommunications equipment. It is a cogeneration system; the heat energy recovered is used to cool the telecommunications equipment. For this system, a method is described for controlling a new DC interconnection converter. Its DC interconnection characteristics are also discussed. The new converter controls its input current to the fuel cell rated current at maximum and can operate stably even when the fuel cell voltage decreases. This allows good DC interconnection characteristics to be obtained in both the steady state and the transient state.

To develop the advanced and rich life, and the also economy and social activity continuously, various types of energy are necessary. At the same time, to protect the global environment and to prevent the depletion of natural resources, the effective and moreover efficient use of energy is becoming important. Electric power is one of the most important forms of energy for our life and society. This paper describes topics and survey results of technical trends regarding the electric power supply systems which are playing a core role as the important infrastructure to support the emergence of information-oriented society. Specifically, the power supply systems that enhance high power quality and reliability (PQR) are important for the steady growth of information and communication services. The direct current (DC) power, which has been used for telecommunications power systems and information and communications technologies (ICT), enables existing utilities' grid and distributed energy resources to keep a balance between supply and demand of small-scaled power systems or microgirds. These techniques are expected to be part of smartgrid technologies and facilitate the installation of distributed generators in mission critical facilities.

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%.