论文标题
模拟基于区块链的电力交易，并在Prosumer Consortium Model中使用太阳能预测
Simulation of Blockchain based Power Trading with Solar Power Prediction in Prosumer Consortium Model
论文作者
论文摘要
Prosumer Consortium Energy交易模型可以成为能源成本的解决方案之一，提高性能，并可以使用分布式发电，向当地团体或社区（例如大学）提供可靠的电力。这项研究证明了基于区块链的功率交易的模拟，并使用两个Prosumer节点中的MLFF神经网络培训补充了太阳能预测。这项研究可能是基于分散的区块链系统实施电力交易市场模型的第一步，该模型具有分布式的大学网格系统。该系统可以平衡研究所内的电力需求和供应，实现安全和快速的交易，并且可以通过预测太阳能生成来加强本地市场系统。 MLFF训练的性能可以预测模型的几乎90％的准确性，因为短期预测。因此，生产商机构可以在交易之前完成决策。
Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a university grid system. This system can balance the electricity demand and supply within the institute, enable secure and rapid transactions, and the local market system can be reinforced by forecasting solar generation. The performance of the MLFF training can predict almost 90% accuracy of the model as short term ahead forecasting. Because of it, the prosumer bodies can complete the decision making before trading to their benefit.
