EnerVMAS: Virtual Agent Organizations to Optimize Energy Consumption Using Intelligent Temperature Calibration

One of the problems we encounter when dealing with the optimization of household energy consumption is how to reduce the consumption of air conditioning systems without reducing the comfort level of the residents. The systems that have been proposed so far do not succeed at optimizing the electricity consumed by heating and air conditioning systems because they do not monitor all the variables involved in this process, often leaving users’ comfort aside. It is therefore necessary to develop a solution that monitors the factors which contribute to greater energy consumption. Such a solution must have a self-adaptive architecture with the capacity of self-organization which will allow it to adapt to changes in user temperature preferences. The methodology that is the most suitable for the development of such solution are virtual agent organizations, they allow for the management of wireless sensor networks (WSN) and the use of Case-Based Reasoning (CBR) for predicting the presence of people at home. This work presents an energy optimization system based on virtual agent organizations (VO-MAS) that obtains the characteristics of the environment through sensors and user behavior pattern using a CBR system. A case study was carried out in order to evaluate the performance of the proposed system, the results show that 22.8% energy savings were achieved.