Towards Energy Efficiency: Forecasting Indoor Temperature via Multivariate Analysis

Abstract

Abstract: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.

Stacked Denoising Auto-Encoders for Short-Term Time Series Forecasting

“…Input signal sequence is sampled at one minute period (indoor temperature time series task presented at [34]), and pre-processed by a low-pass filter to get the mean value of the current plus last 14 samples, introducing a delay of 7 minutes in the predicted values. Hence, s 1 s 2 ... s N are computed, where…”

“…Usually, statistical methods are used to estimate the weight parameters of these models. ANNs have been widely applied to this task [35,2,34], normally trained by one of the different variants of Gradient Descent (GD) algorithm.…”

Evaluation of Visitor Counting Technologies and Their Energy Saving Potential through Demand-Controlled Ventilation

“…The computer based system modified the velocity of the ventilation fans according to the environmental temperature; for example for temperatures of 10 °C and 38 °C the used fan speeds were 1.7 m/s and 5.5 m/s, respectively [15]. Zamora-Martínez et al [16] described an indoor temperature forecasting system based on artificial neural networks. The system utilized various sensors inputs, including CO 2, humidity and temperature.…”

“…The system utilized various sensors inputs, including CO 2, humidity and temperature. As the preliminary tests showed that maintaining the current temperature used only 30%-38.9% of the energy needed to lower the temperature, an accurate forecasting of the indoor temperature could be exploited in energy efficient control of the indoor conditions [16].…”

A Dynamic Model for Indoor Temperature Prediction in Buildings

“…Models based on physical principles can vary largely from extremely complex to more simplified structures with respect to the number of parameters and variables [14][15][16][17] but they usually need detailed information on the building characteristics and are in general too computationally heavy to be effectively used for control purposes. On the other hand, data driven models [18][19][20][21][22][23][24][25][26][27][28] are based solely on measurements and are typically identified without information on the physical nature of the building properties. Hybrid or grey box models are a combination of data driven and physical modelling approaches [29][30][31][32][33][34][35].…”