The small medium large system (SMLsystem) is a house built at the Universidad CEU Cardenal Herrera (CEU-UCH) for participation in the Solar Decathlon 2013 competition. Several technologies have been integrated to reduce power consumption. One of these is a forecasting system based on artificial neural networks (ANNs), which is able to predict indoor temperature in the near future using captured data by a complex monitoring system as the input. A study of the impact on forecasting performance of different covariate combinations is presented in this paper. Additionally, a comparison of ANNs with the standard statistical forecasting methods is shown. The research in this paper has been focused on forecasting the indoor temperature of a house, as it is directly related to HVAC—heating, ventilation and air conditioning—system consumption. HVAC systems at the SMLsystem house represent 53:89% of the overall power consumption. The energy used to maintain temperature was measured to be 30%–38:9% of the energy needed to lower it. Hence, these forecasting measures allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Experimental results show a high forecasting accuracy and therefore, they might be used to efficiently control an HVAC system.

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.

University CEU Cardenal Herrera (CEU-UCH) has constructed a Solar house, known as SMLSystem, to participate in the Solar Decathlon Europe 2012 competition. Such construction becomes a research facility that University employs in order to test innovative solutions around the area of energy efficiency. A lot of technologies have been integrated to help to reduce the overall power consumption of the house. Among them, a predictive system, based on Artificial Neural Networks (ANNs), has been developed using the data acquired in Valencia, where the house is placed. Such system produces short-term forecast of indoor temperature, using as input the data captured by a complex monitoring system. The system expects to reduce the power consumption mainly related to Heating, Ventilation and Air Conditioning (HVAC) systems because of the following assumptions: the high power consumption for which HVAC is responsible (53,9% of the overall consumption); and the energy needed to keep the temperature is less tan the energy required to lower/increase it.This paper studies the viability of the development of such kind of predictive systems but for totally unknown environments, that is, without historical data. To do that it is possible to apply on-line learning approaches, where the model parameters are estimated following Bayesian methods or Gradient Descent (GD) methods, starting from an unbiased a-priori knowledge, or from a totally random model. These forecasting measures could allow the house to adapt itself to future temperature conditions by using home automation in an energy-efficient manner. Preliminary experimental results show a high forecasting accuracy with simple models and with a short training time of 4-5 days. The final idea is to develop intelligent agents, with the minimum resources, to be implemented in very cheap computer architectures.

This work has been supported by the Consolidación de indicadores CEU-UCH2014-15 program of the Vicerrectorado de Investigación at Universidad CEU-Cardenal Herrera and financed by Generalitat Valenciana through Conselleria de Educación, Cultura y Deporte under project GV/2015/088.