Data analytics for occupancy pattern learning to reduce the energy consumption of HVAC systems in office buildings

Abstract In the last few years, the collecting and processing of occupancy data have become emerging issues since they can affect, either directly or indirectly, several energy operations in buildings. The application of data analytics-based methods makes it possible to exploit the potentialities of occupancy related knowledge to enhance the energy management in buildings. A methodology, aimed at implementing an occupancy-based HVAC system operation schedule, is presented in this article. The process is based on the convenience of displacing groups of occupants with similar occupancy patterns to the same thermal zone. An optimisation of the stop schedule of an HVAC system has been investigated, considering a typical week’s occupancy patterns. The methodology was used to analyse the Zaanstad Town Hall (The Netherlands), considering anonymous occupancy data for a monitoring period of four months. The resulting optimised schedule was tested, through an energy simulation approach, considering a model calibrated with real energy consumption data. The savings related to the energy consumption of the HVAC system, as a result of the implementation of the strategy, in comparison to an occupancy-independent operation schedule amounted to 14%. The proposed process can be generalized and drive energy managers in evaluating optimised occupancy-based HVAC system operation schedules.

[1]  Álvaro Sicilia,et al.  Data integration driven ontology design, case study smart city , 2013, WIMS '13.

[2]  Andrew Peacock,et al.  An evidence based approach to determining residential occupancy and its role in demand response management , 2016 .

[3]  Rajib Rana,et al.  Novel activity classification and occupancy estimation methods for intelligent HVAC (heating, ventilation and air conditioning) systems , 2015 .

[4]  Prabir Barooah,et al.  Experimental study of occupancy-based control of HVAC zones ✩ , 2015 .

[5]  Nan Li,et al.  Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations , 2012 .

[6]  Tianzhen Hong,et al.  Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration , 2014, Building and Environment.

[7]  Carlos León,et al.  Rule-based system to detect energy efficiency anomalies in smart buildings, a data mining approach , 2016, Expert Syst. Appl..

[8]  Benjamin C. M. Fung,et al.  Advances and challenges in building engineering and data mining applications for energy-efficient communities , 2016 .

[9]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[10]  Álvaro Sicilia,et al.  MANAGEMENT STRATEGIES FOR THE ENERGY SAVING OF PUBLIC BUILDINGS THROUGH A DECISION SUPPORT SYSTEM , 2016 .

[11]  Donald E. Brown,et al.  Introduction to data mining for medical informatics. , 2008, Clinics in laboratory medicine.

[12]  Edmundas Kazimieras Zavadskas,et al.  Importance of occupancy information when simulating energy demand of energy efficient house: A case study , 2015 .

[13]  Chao Shen,et al.  A review of electric load classification in smart grid environment , 2013 .

[14]  Burcin Becerik-Gerber,et al.  Building occupancy diversity and HVAC (heating, ventilation, and air conditioning) system energy efficiency , 2016 .

[15]  Bing Dong,et al.  A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting , 2013, Building Simulation.

[16]  Siew Eang Lee,et al.  Review of occupancy sensing systems and occupancy modeling methodologies for the application in institutional buildings , 2016 .

[17]  Dimitrios Tzovaras,et al.  Conditional Random Fields - based approach for real-time building occupancy estimation with multi-sensory networks , 2016 .

[18]  I. Budaiwi,et al.  HVAC system operational strategies for reduced energy consumption in buildings with intermittent occupancy: The case of mosques , 2013 .

[19]  Tania Cerquitelli,et al.  Enhancing energy efficiency in buildings through innovative data analytics technologies , 2016 .

[20]  Eva Pongrácz,et al.  Modelling home electricity management for sustainability : the impact of response levels, technological deployment & occupancy , 2016 .

[21]  Costas J. Spanos,et al.  Privacy-Enhanced Architecture for Occupancy-Based HVAC Control , 2016, 2017 ACM/IEEE 8th International Conference on Cyber-Physical Systems (ICCPS).

[22]  Fiorella Lauro,et al.  Fault detection analysis using data mining techniques for a cluster of smart office buildings , 2015, Expert Syst. Appl..

[23]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[24]  Geoffrey Qiping Shen,et al.  Occupancy data analytics and prediction: A case study , 2016 .

[25]  Benjamin C. M. Fung,et al.  Extracting knowledge from building-related data — A data mining framework , 2013, Building Simulation.

[26]  Zheng Yang,et al.  The coupled effects of personalized occupancy profile based HVAC schedules and room reassignment on building energy use , 2014 .

[27]  Qinpeng Wang,et al.  Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings , 2016 .

[28]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[29]  Alfonso Capozzoli,et al.  A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres , 2016 .

[30]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[31]  Fu Xiao,et al.  A framework for knowledge discovery in massive building automation data and its application in building diagnostics , 2015 .

[32]  Pietro Siciliano,et al.  People occupancy detection and profiling with 3D depth sensors for building energy management , 2015 .

[33]  Graeme Flett,et al.  An occupant-differentiated, higher-order Markov Chain method for prediction of domestic occupancy , 2016 .

[34]  Manuel A. Matos,et al.  A new clustering algorithm for load profiling based on billing data , 2012 .

[35]  Neil Brown,et al.  Improved occupancy monitoring in non-domestic buildings , 2017 .

[36]  Francesco Causone,et al.  Estimation models of heating energy consumption in schools for local authorities planning , 2015 .

[37]  Tatjana Vilutiene,et al.  Modelling the Effect of the Domestic Occupancy Profiles on Predicted Energy Demand of the Energy Efficient House , 2013 .

[38]  Thanos G. Stavropoulos,et al.  Occupancy driven building performance assessment , 2016, J. Innov. Digit. Ecosyst..

[39]  Salvatore Carlucci,et al.  The effect of spatial and temporal randomness of stochastically generated occupancy schedules on the energy performance of a multiresidential building , 2016 .

[40]  Zhenghua Chen,et al.  Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings , 2017 .

[41]  Michael E. Webber,et al.  Clustering analysis of residential electricity demand profiles , 2014 .

[42]  Ardeshir Mahdavi,et al.  Predicting people's presence in buildings: An empirically based model performance analysis , 2015 .

[43]  Ilaria Ballarini,et al.  Data structuring for the ontological modelling of urban energy systems: The experience of the SEMANCO project , 2015 .

[44]  Zita Vale,et al.  A data-mining-based methodology to support MV electricity customers’ characterization , 2015 .

[45]  Zheng Yang,et al.  Modeling personalized occupancy profiles for representing long term patterns by using ambient context , 2014 .

[46]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .

[47]  Nanpeng Yu,et al.  Energy Efficient Building HVAC Control Algorithm with Real-time Occupancy Prediction , 2017 .