Power Consumption Estimation using In-Memory Database Computation

Abstract

In order to efficiently predict electricity consumption, we need to improve both the speed and the reliability of computational environment. Concerning the speed, we use inmemory database, which is taught to be the best solution that allows manipulating data many times faster than the hard disk. For reliability, we use machine learning algorithms. Since the model performance and accuracy may vary depending on data each time, we test many algorithms and select the best one. In this study, we use SmartMeter Energy Consumption Data in London Households to predict electricity consumption using machine learning algorithms written in Python programming language and in-memory database computation package, Aerospike. The test results show that the best algorithm for our data set is Bagging algorithm. We also emphatically prove that R-squared may not always be a good test to choose the best algorithm.