Abstract
More and more IoT (Internet of Thing) devices have been connected to our lives in recent years, making life more convenient. Many countries are also making use of Internet of Thing technology to carry out intelligent electricity network reform. One of the reform goals is balancing the supply and demand of electricity, which has become a top priority. Balancing electricity supply and demand through real-time electricity prices has become an effective way. However, using traditional machine learning models for real-time electricity price prediction requires complex feature engineering, and the results are not satisfactory. Also, the mainstream fusion methods use data-level fusion, which will put very high pressure on communication bandwidth and computer resources. In this paper, an LSTM- (long short-term memory-) based decision level fusion of multisource data is proposed and applied for real-time electricity price prediction on actual electricity price datasets. The method solves the difficulties of traditional machine learning models in dealing with complex nonlinear problems. It achieves local asynchronous processing of multisource data through decision-level fusion, reducing the requirement for bandwidth resources and providing perfect results in real-time electricity price prediction. The experimental results show that the prediction accuracy of the decision fusion prediction model based on LSTM is higher than that of the linear regression algorithm.
Highlights
With the development and progress of science and technology, more and more Internet of Things devices are connected to our life
With the reform of the smart grid, the electricity Internet of Things has introduced a variety of latest technologies, such as cloud computing [6], artificial intelligence [7], and big data [8], to realize information perception and processing in all links of the electricity Internet of Things [9]
The results show promising results on the Pennsylvania-New JerseyMaryland (PJM) electricity market dataset
Summary
With the development and progress of science and technology, more and more Internet of Things devices are connected to our life. If the situation is difficult, it may lead to decreased physical function or complete incapacity, so for the hypoxia problem that often occurs in people who work at high altitudes for long periods, Acharya et al [30] proposed a parallel-based decision-level realtime hypoxia monitoring system, based on blood oxygen saturation and dysfunction at different measurements to build a model, tested on a real dataset, and showed outstanding results It has demonstrated its advantages in diagnosing depression. (1) A distributed decision-level fusion method based on power Internet of Things is proposed to predict realtime electricity prices (2) At present, electricity data is multisource and heterogeneous. A distributed data acquisition method of edge cloud is proposed It can effectively obtain information and use the edge server for data processing, reduce the communication cost, and improve timeliness and prediction accuracy (3) The electricity price of different regions is predicted through deep learning.
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