Time Series Optimization for Energy Prediction in Wi-Fi Infrastructures

Abstract

Access points play an important role in Wi-Fi networks and can provide us with useful information about the energy consumption according to the users’ behavior. If we predict the energy consumption in a determined access point, we can make easier the maintenance plans for the network infrastructure making the most adequate decisions about the placement of new devices or reinforcement of existing ones, for example. In this work, we propose an energy prediction methodology based on system identification, where the energy measured in the access points is represented as time series. The prediction results were reasonably good for an experimental environment consisting of ten access points in an academic building, modeling the energy patterns along some weeks. Moreover, we found an optimization problem where the main parameters of the identification model can be adjusted in order to provide results more accurate. Given the computational effort required for searching in depth the optimal values, we applied a genetic algorithm, which provided better results in less time with regard to a direct search method.