Background: With the further development of electric Internet of things (eIoT), IoT devices in the distributed network generate data with different frequencies and types. Objective: Fog platform is located between the smart collected terminal and cloud platform, and the resources of fog computing are limited, which affects the delay of service processing time and response time. Methods: In this paper, an algorithm of fog resource scheduling and load balancing is proposed. First, the fog devices divide the tasks into high or low priority. Then, the fog management nodes cluster the fog nodes through K-mean+ algorithm and implement the earliest deadline first dynamic (EDFD) task scheduling algorithm and De-REF neural network load balancing algorithm. Results: We use tools to simulate the environment, and the results show that this method has strong advantages in -30% response time, -50% scheduling time, delay, -50% load balancing rate and energy consumption, which provides a better guarantee for eIoT. Conclusion: Resource scheduling is important factor affecting system performance. This article mainly addresses the needs of eIoT in terminal network communication delay, connection failure, and resource shortage. And the new method of resource scheduling and load balancing is proposed, The evaluation was performed and proved that our proposed algorithm has better performance than the previous method, which brings new opportunities for the realization of eIoT.

Background: As the "three-type two-net, world-class" strategy proposed, the number of cloud resources in power grid continues to grow, there is a large amount of data to be filed every day. Which are key issues to be addressed, the long-term preservation of data, using back-up data for the operation and maintenance, fault recovery, fault drill and tracking of cloud platform are essential. The traditional compression algorithm faces severe challenges. Method: In this case, this paper proposes the deep-learning method for data compression. First, a more accurate and complete grid cloud resource status data is gathered through data cleaning, correction, and standardization. The preprocessed data is then compressed by SaDE-MSAE. Result: Experiments show that the SaDE-MSAE method can compress data faster. The data compression ratio based on neural network is basically between 45% and 60%, which is relatively stable and stronger than the traditional compression algorithm. Conclusion:: The paper can complete the compressed data quickly and efficiently in a large amount of power data. Improve the speed and accuracy of the algorithm while ensuring that the data is correct and complete, and improve the compression time and efficiency through the neural network. It gives better compression schemes cloud resource data grid.

Background: With the large-scale grid connection operation of new or renewable energy and the access of active loads such as electric vehicles and air conditioners, the electric energy trading business in the power market faces problems such as the rapid expansion of the number of market settlement subjects, explosive growth, various subjects responsible for deviation assessment, various electric energy trading methods and so on. Objective: This paper focuses on the medium and long-term generation side power trading in the new power market. Through cause analysis, induction and summary, algorithm design and case analysis, the problem of generation side deviation prediction is solved and power waste is reduced. Method: This paper puts forward the reasons for the imbalance of medium and long-term power trading in the new power market dominated by new energy, as well as the deviation prediction algorithm based on multi-layer LSTM, which brings the total historical deviation, total planned deviation, total measurement deviation, new energy consumption and other data into the M-LSTM deep learning network for testing in each provincial power market center. Result: We use the neural network prediction algorithm. Compared with a single LSTM, the multi-layer LSTM can better maintain the characteristics of the sample time series and reduce the prediction error. Compared with BPNN、M-BPNN and Cooperative game theory, LSTM has a better memory effect. Conclusion: The experiment shows that the more accurate prediction deviation of this method can better arrange the generation plan, reduce the loss caused by excessive deviation, reduce the "price trampling" of the power market, and ensure the fair and efficient development of the power market.