In this paper, we propose a method to estimate communication performance for the advanced metering infrastructure that employs the power line communication (PLC) technology. Using bit-per-symbol signals from the PLC network management system, we estimate a PLC model quality in terms of packet success rate based on statistical learning. We also verify the accuracy of the estimations by comparing them with measured communication test results at test sites. Finally, from the packet success rate estimate, the qualities of services, such as meter readings and time-of-use pricing data downloading under several metering protocol sequences, are investigated through a mathematical analysis, and numerical results are provided.

In order to construct an efficient on-site communication network for an advanced metering infrastructure (AMI) in Korea, the high-speed power line communication (HS PLC), wireless smart utility network (Wi-SUN), and ZigBee modems are currently being used. In this paper, we first quantitatively analyze the communication performances of HS PLC, Wi-SUN, and ZigBee modems for AMI through both experimental testbeds and practical environment sites. For practical AMI sites, we selected 18 sites with 48 measurement points and classified the sites into five areas, and conducted measurements of signal and noise power spectra on the sites. We then derived linear regression models for received powers according to areas. Through the constructed models, we can efficiently choose an appropriate communication method and plan a methodology for building an AMI network depending on the area type. Furthermore, using the constructed regression models, we provided graphical simulation tools of received powers for both PLC and wireless communication methods based on a distribution information map.

No abstract

No abstract

Demand response (DR) is one of the major benefits that utility companies can derive from the advanced metering infrastructure (AMI). In particular, the dynamic rate plan with DR is attracting attention as an electricity rate system suitable for the future power environment. In order for electricity consumers to select an appropriate electricity rate plan, it is necessary to provide information such as whether electricity bills are reduced by the plan and the estimated amount of electricity bill savings. In this paper, we first comparatively analyze the current progressive rate plan and a dynamic rate plan of the time-of-use (TOU). We next propose several prediction methods for households to provide information on whether the electricity bill amount can be reduced in advance when changing to the TOU rate plan from the progressive rate plan by using only the current monthly electricity usages and bills. In order to develop three different prediction methods based on statistical learning, we use the support vector machine, linear regression, and deep neural network techniques. As a ground truth training sequence, we use hourly electricity usages and bills obtained from ten apartment complexes through AMI, and an apartment complex is used for testing the designed methods. The decision accuracy for the test complex was more than 0.98 and the root mean square error of the saving prediction was 1.7%. INDEX TERMS advanced metering infrastructure (AMI), deep neural network (DNN), linear regression, support vector machine (SVM), progressive rate, time-of-use (TOU) rateNOMENCLATURE 10 DNN Deep neural network. DR Demand response. RMSE Root mean square error. SVM Support vector machine. TOU Time-of-use.