Optimisation of energy management in commercial buildings with weather forecasting inputs: A review

Information about the patterns that govern the energy demand and onsite generation can generate significant savings in the range of 15–30% in most cases and thus is essential for the management of commercial building energy systems. Predominantly, heating and cooling in a building as well as the availability of solar and wind energy are directly affected by variables such as temperature, humidity and solar radiation. This makes energy management decision making and planning sensitive to the prevalent and future weather conditions. Research attempts are being made using a variety of statistical or physical algorithms to predict the evolution of the building load or generation in order to optimise the building energy management. The response of the building energy system to changes in weather conditions is inherently challenging to predict; nevertheless numerous methods in the literature describe and utilise weather predictions. Such methods are being reviewed in this study and their strengths, weaknesses and applications in commercial buildings at different prediction horizons are discussed. Furthermore, the importance of considering weather forecasting inputs in energy management systems is established by highlighting the dependencies of various building components on weather conditions. The issues of the difficulty in implementation of integrated weather forecasts at commercial building level and the potential added value through energy management optimisation are also addressed. Finally, a novel framework is proposed that utilises a range of weather variable predictions in order to optimise certain commercial building systems.

[1]  C. K. Chan,et al.  Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN , 2011 .

[2]  David E. Claridge,et al.  Generalization of the Fourier Series Approach to Model Hourly Energy Use in Commercial Buildings , 1999 .

[3]  K. Mahkamov,et al.  Thermal modelling of the building and its HVAC system using Matlab/Simulink , 2012, 2012 2nd International Symposium On Environment Friendly Energies And Applications.

[4]  Yongjun Sun,et al.  Model-based optimal start control strategy for multi-chiller plants in commercial buildings , 2010 .

[5]  Moncef Krarti,et al.  The impact of forecasting uncertainty on the performance of a predictive optimal controller for thermal energy storage systems , 1999 .

[6]  Anna Bruce,et al.  Peak load characteristics of Sydney office buildings and policy recommendations for peak load reduct , 2011 .

[7]  Ryohei Yokoyama,et al.  Prediction of energy demands using neural network with model identification by global optimization , 2009 .

[8]  A HC van Paassen,et al.  Weather data generator to study climate change on buildings , 2002 .

[9]  Drury B. Crawley,et al.  EnergyPlus: Energy simulation program , 2000 .

[10]  Hani Hagras,et al.  An intelligent agent based approach for energy management in commercial buildings , 2008, 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence).

[11]  José A. Candanedo,et al.  Building simulation weather forecast files for predictive control strategies , 2013, ANSS 2013.

[12]  Zhiwei Lian,et al.  Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique , 2006 .

[13]  Gianpaolo Vitale,et al.  Environmental data processing by clustering methods for energy forecast and planning , 2011 .

[14]  Siaw Kiang Chou,et al.  Development of an energy‐estimating equation for large commercial buildings , 1993 .

[15]  Jesús M. Zamarreño,et al.  A Short-Term Temperature Forecaster Based on a Novel Radial Basis Functions Neural Network , 2001, Int. J. Neural Syst..

[16]  Hao Huang,et al.  Multi-zone temperature prediction in a commercial building using artificial neural network model , 2013, 2013 10th IEEE International Conference on Control and Automation (ICCA).

[17]  Frédéric Magoulès,et al.  A review on the prediction of building energy consumption , 2012 .

[18]  Guillermo Escrivá-Escrivá,et al.  Method for modelling space conditioning aggregated daily load curves: Application to a university building , 2010 .

[19]  Jesús M. Zamarreño,et al.  Prediction of hourly energy consumption in buildings based on a feedback artificial neural network , 2005 .

[20]  Eric Wai Ming Lee,et al.  Short-term prediction of photovoltaic energy generation by intelligent approach , 2012 .

[21]  Peter Palensky,et al.  Modelling and design of a linear predictive controller for a solar powered HVAC system , 2012, 2012 IEEE International Symposium on Industrial Electronics.

[22]  Jianjun Hu,et al.  Model predictive control strategies for buildings with mixed-mode cooling , 2014 .

[23]  Manfred Morari,et al.  A tractable approximation of chance constrained stochastic MPC based on affine disturbance feedback , 2008, 2008 47th IEEE Conference on Decision and Control.

[24]  Jiejin Cai,et al.  Applying support vector machine to predict hourly cooling load in the building , 2009 .

[25]  Kaufui Wong,et al.  Prediction of thermal storage loads using a neural network , 1990 .

[26]  V. Zavala Real-Time Optimization Strategies for Building Systems† , 2013 .

[27]  Kirsten Gram-Hanssen,et al.  2004 ACEEE Summer Study on Energy Efficiency in Buildings , 2004 .

[28]  Jin Wen,et al.  Development and validation of online models with parameter estimation for a building zone with VAV system , 2007 .

[29]  Hiroshi Asano,et al.  Optimal planning of cogeneration systems under time-of-use rates , 1994 .

[30]  L. K. Norford,et al.  Peak load reduction by preconditioning buildings at night , 1991 .

[31]  Zhiwei Lian,et al.  Hourly cooling load prediction by a combined forecasting model based on Analytic Hierarchy Process , 2004 .

[32]  B. Samali,et al.  Component-wise optimization for a commercial central cooling plant , 2011, IECON 2011 - 37th Annual Conference of the IEEE Industrial Electronics Society.

[33]  Yoseba K. Penya,et al.  Efficient building load forecasting , 2011, ETFA2011.

[34]  Yun Li,et al.  Forecasting of photovoltaic power yield using dynamic neural networks , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

[35]  C. Holt Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[36]  Harry Boyer,et al.  Weather data analysis based on typical weather sequence analysis. Application: energy building simulation , 2005 .

[37]  Qi Luo,et al.  Building thermal network model and application to temperature regulation , 2010, 2010 IEEE International Conference on Control Applications.

[38]  Bangyin Liu,et al.  Online 24-h solar power forecasting based on weather type classification using artificial neural network , 2011 .

[39]  Toshio Terai,et al.  An ARMA type weather model for air-conditioning, heating and cooling load calculation , 1991 .

[40]  Xinhua Xu,et al.  A grey‐box model of next‐day building thermal load prediction for energy‐efficient control , 2008 .

[41]  Eric Wai Ming Lee,et al.  An intelligent approach to assessing the effect of building occupancy on building cooling load predi , 2011 .

[42]  Simeng Liu,et al.  Evaluation of reinforcement learning for optimal control of building active and passive thermal storage inventory , 2007 .

[43]  Henrik Madsen,et al.  Short-Term Solar Collector Power Forecasting , 2011 .

[44]  Victor M. Zavala,et al.  On-line economic optimization of energy systems using weather forecast information. , 2009 .

[45]  Gregor P. Henze,et al.  Comparison of Short-Term Weather Forecasting Models for Model Predictive Control , 2009 .

[46]  T. Y. Chen,et al.  Ambient temperature and solar radiation prediction for predictive control of HVAC systems and a methodology for optimal building heating dynamic operation , 1996 .

[47]  Yoseba K. Penya,et al.  Optimal combined short-term building load forecasting , 2011, 2011 IEEE PES Innovative Smart Grid Technologies.

[48]  Lei Shi,et al.  Application of Artificial Neural Network to Predict the Hourly Cooling Load of an Office Building , 2009, 2009 International Joint Conference on Computational Sciences and Optimization.

[49]  Nicolas Morel,et al.  Assessing the total energy impact of manual and optimized blind control in combination with different lighting schedules in a building simulation environment , 2010 .

[50]  Jiejin Cai,et al.  Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks , 2009 .

[51]  Yi Zhang,et al.  SHORT-TERM PREDICTION OF WEATHER PARAMETERS USING ONLINE WEATHER FORECASTS , 2007 .

[52]  Geoffrey E. Hinton,et al.  Learning representations by back-propagation errors, nature , 1986 .

[53]  Bing Dong,et al.  Integrated building control based on occupant behavior pattern detection and local weather forecasting , 2011 .

[54]  P. André,et al.  Optimal heating control in a passive solar commercial building , 2001 .

[55]  Abdullatif Ben-Nakhi,et al.  Cooling load prediction for buildings using general regression neural networks , 2004 .

[56]  Pedro J. Mago,et al.  Real-time combined heat and power operational strategy using a hierarchical optimization algorithm , 2011 .

[57]  Omar M. Al-Rabghi,et al.  Utilizing transfer function method for hourly cooling load calculations , 1997 .

[58]  S. Okamoto A Case Study of Energy Saving by CCHP Technology in a Hospital , 2010 .

[59]  Charles C. Holt,et al.  Author's retrospective on ‘Forecasting seasonals and trends by exponentially weighted moving averages’ , 2004 .

[60]  Li Yan,et al.  Hybrid Genetic Algorithm and Support Vector Regression in Cooling Load Prediction , 2010, 2010 Third International Conference on Knowledge Discovery and Data Mining.

[61]  John L. Wright,et al.  A Simplified Method for Calculating the Effective Solar Optical Properties of a Venetian Blind Layer for Building Energy Simulation , 2009 .

[62]  Gregor P. Henze,et al.  Evaluation of optimal control for active and passive building thermal storage , 2004 .

[63]  Sami Karjalainen,et al.  A state machine approach in modelling the heating process of a building , 2009 .

[64]  James W. Taylor Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles , 2010 .

[65]  Gregor P. Henze,et al.  An investigation of optimal control of passive building thermal storage with real time pricing , 2011 .

[66]  Dimitrios Gyalistras,et al.  Intermediate complexity model for Model Predictive Control of Integrated Room Automation , 2013 .

[67]  Johanna L. Mathieu,et al.  Quantifying Changes in Building Electricity Use, With Application to Demand Response , 2011, IEEE Transactions on Smart Grid.

[68]  Zhang Xu Analysis of Air Conditioning Load Prediction by Modified Seasonal Exponential Smoothing Model , 2005 .

[69]  Jacob H. Stang,et al.  Load prediction method for heat and electricity demand in buildings for the purpose of planning for mixed energy distribution systems , 2008 .

[70]  Li Lanlan,et al.  A novel building cooling load prediction based on SVR and SAPSO , 2010, 2010 International Symposium on Computer, Communication, Control and Automation (3CA).

[71]  Tatsuo Nagai,et al.  A method for revising temperature and humidity prediction using additional observations and weather forecasts , 2007 .

[72]  Gilles Fraisse,et al.  Development of a simplified and accurate building model based on electrical analogy , 2002 .

[73]  D. Claridge,et al.  A Fourier Series Model to Predict Hourly Heating and Cooling Energy Use in Commercial Buildings With Outdoor Temperature as the Only Weather Variable , 1999 .

[74]  David E. Claridge,et al.  Using synthetic data to evaluate multiple regression and principal component analyses for statistical modeling of daily building energy consumption , 1994 .

[75]  Shengwei Wang,et al.  Simplified building model for transient thermal performance estimation using GA-based parameter identification , 2006 .

[76]  Fu Xiao,et al.  Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review , 2013 .

[77]  Roberto Lamberts,et al.  The use of simplified weather data to estimate thermal loads of non-residential buildings , 2004 .

[78]  Henrik W. Bindner,et al.  Model Predictive Controller for Active Demand Side Management with PV self-consumption in an intelligent building , 2012, 2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe).

[79]  Pedro J. Mago,et al.  Building hourly thermal load prediction using an indexed ARX model , 2012 .

[80]  Sabri Ahmad,et al.  Arima model and exponential smoothing method: A comparison , 2013 .

[81]  G. J. Rios-Moreno,et al.  Modelling temperature in intelligent buildings by means of autoregressive models , 2007 .

[82]  Nan Li,et al.  Predicting HVAC Energy Consumption in Commercial Buildings Using Multiagent Systems , 2013 .

[83]  Rob J. Hyndman,et al.  Forecasting time series with multiple seasonal patterns , 2008, Eur. J. Oper. Res..

[84]  Fu Xiao,et al.  Development and validation of a simplified online cooling load prediction strategy for a super high-rise building in Hong Kong , 2013 .

[85]  James E. Braun,et al.  DEVELOPMENT AND APPLICATION OF AN INVERSE BUILDING MODEL FOR DEMAND RESPONSE IN SMALL COMMERCIAL BUILDINGS , 2016 .

[86]  José A. Candanedo,et al.  Model-based predictive control of an ice storage device in a building cooling system , 2013 .

[87]  Bastian Keller,et al.  A Matlab GUI for calculating the solar radiation and shading of surfaces on the earth , 2011, Comput. Appl. Eng. Educ..

[88]  David E. Culler,et al.  Energy-Efficient Building HVAC Control Using Hybrid System LBMPC , 2012, ArXiv.

[89]  Siaw Kiang Chou,et al.  A performance-based method for energy efficiency improvement of buildings , 2011 .

[90]  Louay M. Chamra,et al.  Cost-optimized real-time operation of CHP systems , 2009 .

[91]  Ralph D. Snyder,et al.  Forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal exponential smoothing , 2009 .

[92]  Jie Chen,et al.  Prediction of room temperature and relative humidity by autoregressive linear and nonlinear neural n , 2011 .

[93]  James E. Braun,et al.  Reducing energy costs and peak electrical demand through optimal control of building thermal storage , 1990 .

[94]  Simeng Liu,et al.  Experimental Analysis of Model-Based Predictive Optimal Control for Active and Passive Building Thermal Storage Inventory , 2005 .

[95]  Sohini Roy Chowdhury,et al.  Prediction of electric power consumption for commercial buildings , 2011, The 2011 International Joint Conference on Neural Networks.

[96]  Yoseba K. Penya,et al.  Short-term load forecasting in air-conditioned non-residential Buildings , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[97]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[98]  Isaac Turiel,et al.  Simplified energy analysis methodology for commercial buildings , 1984 .

[99]  Chen Changsong,et al.  Forecasting power output for grid-connected photovoltaic power system without using solar radiation measurement , 2010, The 2nd International Symposium on Power Electronics for Distributed Generation Systems.

[100]  Zhun Yu,et al.  A statistical method for selection of sequences of coincident weather parameters for design cooling load calculations , 2009 .

[101]  Fan Wang,et al.  Developing a weather responsive internal shading system for atrium spaces of a commercial building in tropical climates , 2014 .

[102]  J. W. Taylor,et al.  Short-Term Load Forecasting With Exponentially Weighted Methods , 2012, IEEE Transactions on Power Systems.

[103]  N. Lu,et al.  The temperature sensitivity of the residential load and commercial building load , 2009, 2009 IEEE Power & Energy Society General Meeting.

[104]  Moncef Krarti,et al.  Planning horizon for a predictive optimal controller for thermal energy storage systems , 1999 .

[105]  I. Dincer,et al.  A new approach for predicting cooling degree-hours and energy requirements in buildings , 2011 .

[106]  Gang Xu,et al.  Particle Swarm Optimization-based LS-SVM for Building Cooling Load Prediction , 2010, J. Comput..

[107]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[108]  Cheol-Yong Jang,et al.  Feasibility study on a novel methodology for short-term real-time energy demand prediction using weather forecasting data , 2013 .

[109]  G. Mustafaraj,et al.  Development of room temperature and relative humidity linear parametric models for an open office using BMS data , 2010 .

[110]  Jirí Cigler,et al.  Subspace identification and model predictive control for buildings , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[111]  Chris Marnay,et al.  Electric storage in California’s commercial buildings , 2013 .

[112]  Tatsuo Nagai,et al.  Optimization method for minimizing annual energy, peak energy demand, and annual energy cost through use of building thermal storage , 2002 .

[113]  Le Xie,et al.  A novel ARX-based multi-scale spatio-temporal solar power forecast model , 2012, 2012 North American Power Symposium (NAPS).

[114]  James E. Braun,et al.  Evaluating the Performance of Building Thermal Mass Control Strategies , 2001 .

[115]  Sean Danaher,et al.  Application of an Artificial Neural Network for Modelling the Thermal Dynamics of a Building’s Space and its Heating System , 2002 .

[116]  Govindasamy TamizhMani,et al.  Photovoltaic performance models: an evaluation with actual field data , 2008, Optics + Photonics for Sustainable Energy.

[117]  François Maréchal,et al.  Predictive optimal management method for the control of polygeneration systems , 2009, Comput. Chem. Eng..

[118]  Hua-Tsung Chen,et al.  Thermal Model Based Power-Generated Prediction by Using Meteorological Data in BIPV System , 2011 .

[119]  James E. Braun,et al.  An Inverse Gray-Box Model for Transient Building Load Prediction , 2002 .

[120]  Eric Wai Ming Lee,et al.  A study of the importance of occupancy to building cooling load in prediction by intelligent approach , 2011 .

[121]  Constantinos A. Balaras,et al.  Development of a neural network heating controller for solar buildings , 2000, Neural Networks.

[122]  Dennis L. Loveday,et al.  Stochastic modelling of temperatures for a full-scale occupied building zone subject to natural random influences , 1993 .

[123]  James W. Taylor Reply to the discussion of: Exponentially weighted methods for forecasting intraday time series with multiple seasonal cycles , 2010 .

[124]  J. W. Macarthur,et al.  On-line recursive estimation for load profile prediction , 1989 .

[125]  Kartik B. Ariyur,et al.  Real Time Energy Management: Cutting the Carbon Footprint and Energy Costs via Hedging, Local Sources and Active Control , 2009 .

[126]  António E. Ruano,et al.  Prediction of building's temperature using neural networks models , 2006 .

[127]  Saifur Rahman,et al.  Forecasting sub-hourly solar irradiance for prediction of photovoltaic output , 1987 .

[128]  Pankaj K. Sen,et al.  Estimation of electricity consumption in commercial buildings , 2011, 2011 North American Power Symposium.

[129]  C. E. Dorgan,et al.  Optimizing system control with load prediction by neural networks for an ice-storage system , 1996 .

[130]  F. W. Yu,et al.  Climatic influence on the design and operation of chiller systems serving office buildings in a subtropical climate , 2012 .

[131]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[132]  Jonathan A. Wright,et al.  Adaptive Diurnal Prediction of Ambient Dry-Bulb Temperature and Solar Radiation , 2002 .

[133]  Jin Yang,et al.  On-line building energy prediction using adaptive artificial neural networks , 2005 .

[134]  Paul Raftery,et al.  Calibrating whole building energy models: Detailed case study using hourly measured data , 2011 .

[135]  Li Lanlan,et al.  Hybrid support vector machine and ARIMA model in building cooling prediction , 2010, 2010 International Symposium on Computer, Communication, Control and Automation (3CA).

[136]  James H. Garrett,et al.  Engineering applications of neural networks , 1993, J. Intell. Manuf..

[137]  Mike Duke,et al.  Studies of control strategies for Building Integrated Solar Energy System , 2011, 2011 IEEE Conference on Clean Energy and Technology (CET).

[138]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[139]  Andrew Kusiak,et al.  A data-driven approach for steam load prediction in buildings , 2010 .

[140]  Atsushi Yamada,et al.  On-line prediction for load profile of an air-conditioning system , 1995 .

[141]  Kyoung-ho Lee,et al.  Reducing Peak Cooling Loads through Model-Based Control of Zone Temperature Setpoints , 2007, 2007 American Control Conference.

[142]  M. Piette,et al.  Peak Demand Reduction from Pre-Cooling with Zone Temperature Reset in an Office Building , 2004 .

[143]  Kwang-Woo Kim,et al.  Prediction of the time of room air temperature descending for heating systems in buildings , 2004 .

[144]  J. W. Taylor,et al.  Short-term electricity demand forecasting using double seasonal exponential smoothing , 2003, J. Oper. Res. Soc..

[145]  Yun Kyu Yi,et al.  A NEW METHOD FOR PREDICTING MIXED-USE BUILDING ENERGY: THE USE OF SIMULATION TO DEVELOP STATISTICAL MODELS , 2013 .

[146]  António E. Ruano,et al.  Model Based Predictive Control of HVAC Systems for Human Thermal Comfort and Energy Consumption Minimisation , 2012, CESCIT.

[147]  Linda Pedersen,et al.  Use of different methodologies for thermal load and energy estimations in buildings including meteorological and sociological input parameters , 2007 .

[148]  Manfred Morari,et al.  Use of model predictive control and weather forecasts for energy efficient building climate control , 2012 .

[149]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[150]  Moncef Krarti,et al.  Optimal control of building storage systems using both ice storage and thermal mass – Part I: Simulation environment , 2012 .

[151]  H. Manz,et al.  Empirical validation of models to compute solar irradiance on inclined surfaces for building energy simulation , 2007 .

[152]  Les E. Shephard,et al.  Coupling Simulation Tools and Real-Time Data to Improve Building Energy Performance , 2013, 2013 IEEE Green Technologies Conference (GreenTech).

[153]  Per Fahlén,et al.  Estimation of operative temperature in buildings using artificial neural networks , 2006 .

[154]  Victor M. Zavala,et al.  Economic impacts of advanced weather forecasting on energy system operations , 2010, 2010 Innovative Smart Grid Technologies (ISGT).

[155]  Moncef Krarti,et al.  Development of a Predictive Optimal Controller for Thermal Energy Storage Systems , 1997 .

[156]  Michaël Kummert,et al.  A neural network controller for hydronic heating systems of solar buildings , 2004, Neural Networks.