Indoor Air-Temperature Forecast for Energy-Efficient Management in Smart Buildings

In the last few years, the reduction of energy consumption and pollution became mandatory. It became also a common goal of many countries. Only in Europe, the building sector is responsible for the total 40% of energy consumption and 36% of $CO_{2}$ pollution. Therefore, new control policies based on the forecast of buildings energy behaviors can be developed to reduce energy waste (i.e. policies for Demand Response and Demand Side Management). This paper discusses an innovative methodology for smart building indoor air-temperature forecasting. This methodology is based on a Non-linear Autoregressive neural network. This neural network has been trained and validated with a dataset consisting of six years indoor air-temperature values of a building demonstrator. In detail, we have studied three characterizing rooms and the whole building. Experimental results of energy prediction are presented and discussed.

[1]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[2]  Sophie Papst.  Advanced Building Simulation , 2016 .

[3]  Giansalvo Cirrincione,et al.  Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions , 2018, SMARTGREENS.

[4]  Murat Kulahci,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[5]  Sheldon X.-D. Tan,et al.  Parameterized architecture-level dynamic thermal models for multicore microprocessors , 2010, TODE.

[6]  Pierluigi Siano,et al.  Demand response and smart grids—A survey , 2014 .

[7]  Song Han,et al.  Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.

[8]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[9]  Igor Mezic,et al.  Extracting Dynamic Information From Whole-Building Energy Models , 2012 .

[10]  Niels Kjølstad Poulsen,et al.  NNSYSID-Toolbox for System Identification with Neural Networks , 2002 .

[11]  Sheldon X.-D. Tan,et al.  General Parameterized Thermal Modeling for High-Performance Microprocessor Design , 2012, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[12]  S. Roaf,et al.  Standards for Thermal Comfort: Indoor air temperature standards for the 21st century , 1995 .

[13]  Enrico Macii,et al.  Building Energy Modelling and Monitoring by Integration of IoT Devices and Building Information Models , 2017, 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC).

[14]  Xin Li,et al.  Learning based compact thermal modeling for energy-efficient smart building management , 2015, 2015 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[15]  Elisa Guelpa,et al.  IoT Software Infrastructure for Energy Management and Simulation in Smart Cities , 2017, IEEE Transactions on Industrial Informatics.

[16]  W Wim Zeiler,et al.  Personalized conditioning and its impact on thermal comfort and energy performance - A review , 2014 .

[17]  Kody M. Powell,et al.  Reduced-order residential home modeling for model predictive control , 2014 .

[18]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[19]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[20]  R. Rajamani.  Observers for Lipschitz nonlinear systems , 1998, IEEE Trans. Autom. Control..

[21]  Christian A. Gueymard,et al.  A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects , 2014 .

[22]  James E. Braun,et al.  REDUCED-ORDER BUILDING MODELING FOR APPLICATION TO MODEL-BASED PREDICTIVE CONTROL , 2012 .

[23]  Jan Hensen,et al.  Building Performance Simulation for Design and Operation , 2019 .

[24]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[25]  Hava T. Siegelmann,et al.  Computational capabilities of recurrent NARX neural networks , 1997, IEEE Trans. Syst. Man Cybern. Part B.

[26]  Antonello Monti,et al.  Optimal Scheduling of Heat Pumps for Power Peak Shaving and Customers Thermal Comfort , 2017, SMARTGREENS.