Multi-Channel Data Stream Transmission Method of Internet of Things in Power Systems (IOTIPS) Based on Big Data Analysis

To solve the problems of frequent network link jitter and high bit error rate is the development direction of power grid communication technology. Therefore, a multi-channel data stream transmission method of Internet of things in power systems based on big data analysis is proposed. The data stream matching method based on big data stability mechanism is constructed by using data stream matching method to match the data stream to be transmitted and improve the anti-noise performance of the transmission process; the multichannel model of data stream transmission is constructed, and the matched data stream is transmitted by the multi-channel model; the big data analysis technology is used to process the data stream transmission process and improve the transmission performance of the model; the adaptive multi-channel equalization control method of sampling decision is used to realize the equalization design of data stream transmission channel, optimize the model transmission process, and reduce the bit error rate of transmission. Experimental results show that this method has better channel equalization performance; the link jitter frequency of this method is low, and it has better transmission stability; the lowest bit error rate can reach 0%, and the reliability of data stream transmission is high.

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Data-Driven Load Forecasting of Air Conditioners for Demand Response Using Levenberg–Marquardt Algorithm-Based ANN

Big Data and Cognitive Computing ◽

10.3390/bdcc3030036 ◽

2019 ◽

Vol 3 (3) ◽

pp. 36 ◽

Cited By ~ 2

Author(s):

Muhammad Waseem ◽

Zhenzhi Lin ◽

Li Yang

Keyword(s):

Big Data ◽

Data Analysis ◽

Power Systems ◽

Demand Response ◽

Load Forecasting ◽

Big Data Analysis ◽

Statistical Regression ◽

Air Conditioners ◽

Marquardt Algorithm ◽

Levenberg Marquardt

Air Conditioners (AC) impact in overall electricity consumption in buildings is very high. Therefore, controlling ACs power consumption is a significant factor for demand response. With the advancement in the area of demand side management techniques implementation and smart grid, precise AC load forecasting for electrical utilities and end-users is required. In this paper, big data analysis and its applications in power systems is introduced. After this, various load forecasting categories and various techniques applied for load forecasting in context of big data analysis in power systems have been explored. Then, Levenberg–Marquardt Algorithm (LMA)-based Artificial Neural Network (ANN) for residential AC short-term load forecasting is presented. This forecasting approach utilizes past hourly temperature observations and AC load as input variables for assessment. Different performance assessment indices have also been investigated. Error formulations have shown that LMA-based ANN presents better results in comparison to Scaled Conjugate Gradient (SCG) and statistical regression approach. Furthermore, information of AC load is obtainable for different time horizons like weekly, hourly, and monthly bases due to better prediction accuracy of LMA-based ANN, which is helpful for efficient demand response (DR) implementation.

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