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access icon openaccess Machine learning algorithm for activity-aware demand response considering energy savings and comfort requirements

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 13/09/2019, Accepted 08/06/2020, Revised 08/05/2020, Published 07/09/2020

Due to the high cost of peak hour power generation and a push towards sustainability, the need for demand response (DR) is increasing. Compared to commercial-level DR, residential-level DR is more challenging. Residents are reluctant to participate, and DR controllers lack sufficient real-time activity information to balance energy savings with residents' need for comfort and convenience. To address the above challenges, we propose a sensor data-driven activity-based controller for heating, ventilation, and air conditioning devices. Using our proposed novel strategy, resident activities are recognized in real-time through a random forest machine learning approach. Integrating activity information and forecasted electricity pricing, the proposed controller can simultaneously reduce energy consumption for sustainability and maintain resident constraints for comfort based on recognized activities. Results demonstrate the superiority of the proposed approach.

Inspec keywords: HVAC; power generation control; power consumption; power engineering computing; demand side management; learning (artificial intelligence); energy conservation; building management systems

Other keywords: residential-level DR; real-time activity information; comfort requirements; sensor data-driven activity-based controller; activity-aware demand response; random forest machine; energy savings; machine learning algorithm; resident constraints; air conditioning devices; heating-ventilation-air conditioning devices; commercial-level DR; DR controllers; peak hour power generation; energy consumption

Subjects: Air conditioning; Power system control; Control of heat systems; Energy conservation; Buildings (energy utilisation); Power engineering computing; Refrigeration and cooling (energy utilisation); Power system management, operation and economics; Neural computing techniques

References

    1. 1)
      • 14. Samad, T., Koch, E., Stluka, P.: ‘Automated demand response for smart buildings and microgrids: the state of the practice and research challenges’, Proc. IEEE, 2016, 104, pp. 726–744.
    2. 2)
      • 49. Heydt, G.T., Chowdhury, B.H., Crow, M.L., et al:, ‘Pricing and control in the next generation power distribution system’, IEEE Trans. Smart Grid, 2012, 3, pp. 907–914.
    3. 3)
      • 40. Zhang, Y., Beaudin, M., Taheri, R., et al: ‘Day-ahead power output forecasting for small-scale solar photovoltaic electricity generators’, IEEE Trans. Smart Grid, 2015, 6, pp. 2253–2262.
    4. 4)
      • 41. Reikard, G.: ‘Predicting solar radiation at high resolutions: A comparison of time series forecasts’, Sol. Energy, 2009, 83, (3), pp. 342–349.
    5. 5)
      • 17. Farahani, S.S.S., Tabar, M.B., Tourang, H., et al: ‘Using exponential modeling for DLC demand response programs in electricity markets’, Res. J. Appl. Sci. Eng. Technol., 2012, 4, (2012), pp. 749–753.
    6. 6)
      • 36. Singla, G., Cook, D.J., Schmitter-Edgecombe, M.: ‘Recognizing independent and joint activities among multiple residents in smart environments’, J. Ambient. Intell. Humaniz. Comput., 2010, 1, pp. 57–63.
    7. 7)
      • 42. Zhang, Y., Beaudin, M., Zareipour, H., et al: ‘Forecasting solar photovoltaic power production at the aggregated system level’. 2014 North Am. Power Symp., Pullman, WA, USA., September 2014, pp. 1–6.
    8. 8)
      • 2. Bassamzadeh, N., Ghanem, R., Lu, S., et al: ‘Robust scheduling of smart appliances with uncertain electricity prices in a heterogeneous population’, Energy Build., 2014, 84, pp. 537–547.
    9. 9)
      • 27. Ma, Y., Ghasemzadeh, H.: ‘Labelforest: non-parametric semi-supervised learning for activity recognition’. AAAI, Honolulu, HI, USA., 2019.
    10. 10)
      • 46. Shariatzadeh, F., Srivastava, A. K.: ‘Look-ahead control approach for thermostatic electric load in distribution system’. 2013 North American Power Symp. (NAPS), Manhattan, KS, USA., September 2013, pp. 1–6.
    11. 11)
      • 12. Mathieu, J.L., Price, P.N., Kiliccote, S., et al: ‘Quantifying changes in building electricity use, with application to demand response’, IEEE Trans. Smart Grid, 2011, 2, pp. 507–518.
    12. 12)
      • 13. Tascikaraoglu, A., Paterakis, N.G., Erdinc, O., et al: ‘Combining the flexibility from shared energy storage systems and dlc-based demand response of hvac units for distribution system operation enhancement’, IEEE Trans. Sustain. Energy, 2019, 10, pp. 137–148.
    13. 13)
      • 28. Hu, N., Englebienne, G., Lou, Z., et al: ‘Learning to recognize human activities using soft labels’, IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39, pp. 1973–1984.
    14. 14)
      • 26. Cao, L., Wang, Y., Zhang, B., et al: ‘Gchar: an efficient group-based context-aware human activity recognition on smartphone’, J. Parallel Distrib. Comput., 2016, 12, pp. 67–80.
    15. 15)
      • 10. Sivaneasan, B., Nandha Kumar, K., Tan, K. T., et al: ‘Preemptive demand response management for buildings’, IEEE Trans. Sustain. Energy, 2015, 6, pp. 346–356.
    16. 16)
      • 16. Hamidi, V., Li, F., Robinson, F.: ‘Demand response in the UK's domestic sector’, Electr. Power Syst. Res., 2009, 79, (12), pp. 1722–1726.
    17. 17)
      • 25. Hwang, K., Lee, S.: ‘Environmental audio scene and activity recognition through mobile-based crowdsourcing’, IEEE Trans. Consum. Electron., 2012, 58, pp. 700–705.
    18. 18)
      • 5. Shariatzadeh, F., Mandal, P., Srivastava, A. K.: ‘Demand response for sustainable energy systems: a review, application and implementation strategy’, Renew. Sust. Energy Rev., 2015, 45, pp. 343–350.
    19. 19)
      • 24. Hasan, M., Roy-Chowdhury, A.K.: ‘Context aware active learning of activity recognition models’. Proc. of the 2015 IEEE Int. Conf. on Computer Vision (ICCV), ICCV ‘15, IEEE Computer Society, Washington, DC, USA, 2015, pp. 4543–4551.
    20. 20)
      • 39. Box, G.E.P., Jenkins, G.M., Reinsel, G.C., et al: ‘Time series analysis: forecasting and control’ (John Wiley & Sons, USA., 2015).
    21. 21)
      • 29. Yan, S., Lin, K., Zheng, X., et al: ‘Using latent knowledge to improve real-time activity recognition for smart iot’, IEEE Trans. Knowl. Data Eng., 2019, 32, pp. 1–1.
    22. 22)
      • 38. Krishnan, N.C., Cook, D.J.: ‘Activity recognition on streaming sensor data’, Pervasive Mob. Comput., 2014, 10, (Part B), pp. 138–154.
    23. 23)
      • 50. Cook, D.J., Krishnan, N.: ‘Mining the home environment’, J. Intell. Inf. Syst., 2014, 43, (3), pp. 503–519.
    24. 24)
      • 9. Elamin, W.E., Shaaban, M.F.: ‘New real-time demand-side management approach for energy management systems’, IET Smart Grid, 2019, 2, (2), pp. 183–191.
    25. 25)
      • 43. Katipamula, S., Lu, N.: ‘Evaluation of residential HVAC control strategies for demand response programs’, Trans. Soc. Heat. Refrig. AIR Cond. Eng., 2006, 112, (1), p. 535.
    26. 26)
      • 37. Aminikhanghahi, S., Cook, D.J.: ‘Enhancing activity recognition using cpdbased activity segmentation’, Pervasive Mob. Comput., 2019, 53, pp. 75–89.
    27. 27)
      • 47. Beyer, H.-G., Sendhoff, B.: ‘Robust optimization – a comprehensive survey’, Comput. Methods Appl. Mech. Eng., 2007, 196, (33), pp. 3190–3218.
    28. 28)
      • 45. Taylor, Z.T., Gowri, K., Katipamula, S.: ‘Gridlab-d technical support document: Residential end-use module version 1.0’, tech. rep., Pacific Northwest Lab., Richland, WA (USA), 2008.
    29. 29)
      • 11. Zhou, Z., Zhao, F., Wang, J.: ‘Agent-based electricity market simulation with demand response from commercial buildings’, IEEE Trans. Smart Grid, 2011, 2, pp. 580–588.
    30. 30)
      • 8. Yammani, C., Prabhat, P.: ‘Collaborative demand response in smart electric grid with virtual system operator’, IET Smart Grid, 2018, 1, (3), pp. 76–84.
    31. 31)
      • 6. Harish, V., Kumar, A.: ‘Demand side management in India: action plan, policies and regulations’, Renew. Sustain. Energy Rev., 2014, 33, pp. 613–624.
    32. 32)
      • 4. Dong, J., Xue, G., Li, R.: ‘Demand response in China: regulations, pilot projects and recommendations-A review’, Renew. Sustain. Energy Rev., 2016, 59, pp. 13–27.
    33. 33)
      • 20. Siano, P.: ‘Demand response and smart grids – survey’, Renew. Sustain. Energy Rev., 2014, 30, pp. 461–478.
    34. 34)
      • 23. Sonmez, D., Dincer, K.: ‘A review of modern residential thermostats for home automation to provide energy efficiency’. 2016 4th Int. Istanbul Smart Grid Congress and Fair (ICSG), Istanbul, Turkey, April 2016, pp. 1–4.
    35. 35)
      • 34. van Kasteren, T., Krose, B.: ‘Bayesian activity recognition in residence for elders’. IET Conf. Proc., (3), January 2007, pp. 209–212.
    36. 36)
      • 15. Gyamfi, S., Krumdieck, S., Urmee, T.: ‘Residential peak electricity demand response–highlights of some behavioural issues’, Renew. Sustain. Energy Rev., 2013, 25, pp. 71–77.
    37. 37)
      • 32. Bao, L., Intille, S.S.: ‘Activity recognition from user-annotated acceleration data’, in Ferscha, A., Mattern, F. (Eds.): ‘Pervasive computing’ (Springer Berlin Heidelberg, Berlin, Heidelberg, 2004), pp. 1–17.
    38. 38)
      • 18. Hansen, T.M., Chong, E.K.P., Suryanarayanan, S., et al: ‘A partially observable markov decision process approach to residential home energy management’, IEEE Trans. Smart Grid, 2018, 9, pp. 1271–1281.
    39. 39)
      • 48. Minor, B.D.: ‘Prediction of inhabitant activities in smart environments’, PhD Thesis, Washington State University, 2015.
    40. 40)
      • 7. Alasseri, R., Tripathi, A., Rao, T. J., et al: ‘A review on implementation strategies for demand side management (DSM) in Kuwait through incentive-based demand response programs’, Renew. Sustain. Energy Rev., 2017, 77, (September), pp. 617–635.
    41. 41)
      • 30. Wang, J., Chen, Y., Hao, S., et al: ‘Deep learning for sensor-based activity recognition: a survey’, Pattern Recognit. Lett., 2019, 119, pp. 3–11, Deep Learning for Pattern Recognition.
    42. 42)
      • 31. Foerster, F., Smeja, M., Fahrenberg, J.: ‘Detection of posture and motion by accelerometry: a validation study in ambulatory monitoring’, Comput. Hum. Behav., 1999, 15, (5), pp. 571–583.
    43. 43)
      • 3. Xu, Q., Ji, Y., Huang, Q., et al: ‘Bi-level optimized dispatch strategy of electric supply-demand balance considering risk-benefit coordination’, IET Smart Grid, 2018, 1, (4), pp. 169–176.
    44. 44)
      • 1. U.S. Energy Information Administration: ‘Residential energy consumption survey’, 2017. Available at www.eia.gov.
    45. 45)
      • 21. Erdinc, O., Tascikaraoglu, A., Paterakis, N.G., et al: ‘Enduser comfort oriented day-ahead planning for responsive residential hvac demand aggregation considering weather forecasts’, IEEE Trans. Smart Grid, 2017, 8, pp. 362–372.
    46. 46)
      • 19. Ahmed, M.S., Mohamed, A., Homod, R.Z., et al: ‘Hybrid lsa-ann based home energy management scheduling controller for residential demand response strategy’, Energies, 2016, 9, (9), p. 716.
    47. 47)
      • 35. Lester, J., Choudhury, T.: ‘A hybrid discriminative/generative approach for modeling human activities’. Proc. of the 19th int. joint Conf. on artificial intelligence, Edinburgh, Scotland, 01 2005, pp. 7662–7772.
    48. 48)
      • 33. Ravi, N., Dandekar, N., Mysore, P., et al: ‘Activity recognition from accelerometer data’, Aaai, 2005, 5, pp. 1541–1546.
    49. 49)
      • 22. Babar, M., Ahamed, T. P. I., Al-Ammar, E. A., et al: ‘A novel algorithm for demand reduction bid based incentive program in direct load control’, Energy Proc., 2013, 42, pp. 607–613.
    50. 50)
      • 44. Pratt, R.G., Conner, C.C., Drost, M.K., et al: ‘Significant ELCAP analysis results: Summary report.[end-use load and consumer assessment program]’, tech. rep., Pacific Northwest Lab., Richland, WA (USA), 1991.
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