2007 Volume 2 Issue 2 Pages 256-267
The bioethanol reforming system (FBSR) using sunlight as a heat source is a fuel production system for fuel cells with little environmental impact. However, because solar radiation and outside air temperature are unstable, it is difficult to predict operation of the system with accuracy. Therefore, an operation prediction program of the FBSR using a layered neural network (NN) with the error-correction learning method has been developed. We developed a method of analyzing the operation of a natural energy system with sufficient accuracy. The weather pattern (the amount of global solar radiation and the outside air temperature) and energy-demand pattern for the past one year are inputted into the NN. Moreover, training signals are calculated by a genetic algorithm (GA). The training signals are given to the NN, and the operation pattern of the FBSR is made to learn. Operation of the FBSR on arbitrary days can be predicted by inputting the weather pattern and the energy-demand pattern into this learning NN. In this paper, the operation prediction program of the FBSR is developed, and details of the analytic accuracy are clarified. As a result of analyzing using the developed algorithm, when ±20% or less of power load fluctuation occurred, the operation plan was analyzable in 14% or less of error span. On the other hand, in operation prediction when ±50% or less of fluctuation is added to the outside temperature and global solar radiation, there was 16% or less analysis error.
