Missing data solution of electricity consumption based on Lagrange Interpolation case study: IntelligEnSia data monitoring

Missing data or values is a common issue in processing a dataset. It is also occurred in our IntelligEnSia system, which is a system that utilizes and optimizes the electricity consumption data. The problems occur when the data that are being sent by the sensor(s) to the web server are missing due to the unstable internet connection. It is an essential matter, since we want to capture the data by real time. The data set are useful to learn the pattern of the electricity consumption and predict the next electricity demand. Therefore, to overcome these problems we try to propose a method to complete the missing data by applying Lagrange Interpolating polynomial method. The missing data can be interpolated by using the first-order, second-order and third-order of Lagrange interpolation and in determining the pattern data; we applied PB's eye technique, which is an improved technique of Lagrange Interpolating polynomial method. This research then may support to predict the electricity consumption and to create an effective prediction model.