Building Simulation

, Volume 12, Issue 6, pp 1033–1045 | Cite as

A hybrid energy management approach for home appliances using climatic forecasting

  • Jasmeet KaurEmail author
  • Anju Bala
Research Article


Energy management refers to saving the power by employing effective monitoring and control strategies. The demand for energy is rising in all sectors, such as residential, industrial, transportation, and agriculture, owing to our dependency on electronic appliances. Hence, of late, energy management in the households has become a pertinent issue. Electricity consumption depends on various factors, including climatic conditions, number of occupants in the household and their behavior, usage of appliances, etc. The utilization of electronic appliances and the climate conditions are inter-related; if the outside temperature is high, the usage of ACs in the house increases and vice-versa. Therefore, the climatic conditions are the most relevant factors in energy consumption. There is a need to manage the energy demand by using certain optimization approaches and by predicting the demand based on different climatic conditions. In this study, the prediction model of Artificial Neural Network (ANN) was merged with the optimization methods such as Particle Swarm Optimization (ANN-PSO) and Artificial Bee Colony (ANN-ABC). The experimental results revealed that ANN-ABC performed in a superior manner by reducing the energy consumption by up to 41.12 kWh per day. Finally, the prediction results were compared with the existing model and it was verified that the energy prediction with climatic conditions gave the better results of the power usage in the households.


energy forecasting particle swarm optimization artificial bee colony artificial neural network 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Ahmad AS, Hassan MY, Abdullah MP, Rahman HA, Hussin F, Abdullah H, Saidur R (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33: 102–109.CrossRefGoogle Scholar
  2. Ardakani FJ, Ardehali MM (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65: 452–461.CrossRefGoogle Scholar
  3. Balshi MS, McGuire AD, Duffy P, Flannigan M, Walsh J, Melillo J (2009). Assessing the response of area burned to changing climate in Western boreal North America using a Multivariate Adaptive Regression Splines (MARS) approach. Global Change Biology, 15: 578–600.CrossRefGoogle Scholar
  4. Biswas MAR, Robinson MD, Fumo N (2016). Prediction of residential building energy consumption: A neural network approach. Energy, 117: 84–92.CrossRefGoogle Scholar
  5. Candanedo LM, Feldheim V, Deramaix D (2017). Data driven prediction models of energy use of appliances in a low-energy house. Energy and Buildings, 140: 81–97.CrossRefGoogle Scholar
  6. Chen Y, Xu P, Chu Y, Li W, Wu Y, Ni L, Bao Y, Wang K (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings. Applied Energy, 195: 659–670.CrossRefGoogle Scholar
  7. Cottone P, Gaglio S, Re GL, Ortolani M (2015). User activity recognition for energy saving in smart homes. Pervasive and Mobile Computing, 16: 156–170.CrossRefGoogle Scholar
  8. Divina F, Gilson A, Goméz-Vela F, García Torres M, Torres J (2018). Stacking ensemble learning for short-term electricity consumption forecasting. Energies, 11(4): 949.CrossRefGoogle Scholar
  9. Do H, Cetin KS (2018). Residential building energy consumption: a review of energy data availability, characteristics, and energy performance prediction methods. Renewable Energy Reports, 5: 76–85.Google Scholar
  10. Dong B, Li Z, Rahman SM, Vega R (2016). A hybrid model approach for forecasting future residential electricity consumption. Energy and Buildings, 117: 341–351.CrossRefGoogle Scholar
  11. Eberhart R, Kennedy J (1995). A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science (MHS’95), Nagoya, Japan, pp. 39–43.Google Scholar
  12. Gajowniczek K, Ząbkowski T (2017). Electricity forecasting on the individual household level enhanced based on activity patterns. PLoS One, 12: e0174098.CrossRefGoogle Scholar
  13. Godina R, Rodrigues E, Pouresmaeil E, Matias J, Catalão J (2016). Model predictive control technique for energy optimization in residential sector. In: Proceedings of the 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy.CrossRefGoogle Scholar
  14. Gupta N, Ahuja N, Malhotra S, Bala A, Kaur G (2017). Intelligent heart disease prediction in cloud environment through ensembling. Expert Systems, 34 (3): e12207.CrossRefGoogle Scholar
  15. Ha DL, Joumaa H, Ploix S, Jacomino M (2012). An optimal approach for electrical management problem in dwellings. Energy and Buildings, 45: 1–14.CrossRefGoogle Scholar
  16. Juan Y-K, Gao P, Wang J (2010). A hybrid decision support system for sustainable office building renovation and energy performance improvement. Energy and Buildings, 42: 290–297.CrossRefGoogle Scholar
  17. Karaboga D (2005). An idea based on honey bee swarm for numerical optimization. Technical report-tr06. Erciyes University, Turkey.Google Scholar
  18. Karaboga D, Akay B (2007). Artificial Bee Colony (ABC) algorithm on training artificial neural networks. In: Proceedings of IEEE 15th Signal Processing and Communications Applications (SIU), Eskisehir, Turkey.CrossRefGoogle Scholar
  19. Kaur J, Bala A (2018). Predicting power for home appliances based on climatic conditions. International Journal of Energy Sector Management,
  20. Kaur J, Bala A (2019). Review of machine learning techniques for optimizing energy of home appliances. In: Fong S, Akashe S, Mahalle PN (eds), Information and Communication Technology for Competitive Strategies. Singapore: Springer Singapore. pp. 255–263.CrossRefGoogle Scholar
  21. Khakimova A, Kusatayeva A, Shamshimova A, Sharipova D, Bemporad A, Familiant Y, Shintemirov A, Ten V, Rubagotti M (2017). Optimal energy management of a small-size building via hybrid model predictive control. Energy and Buildings, 140: 1–8.CrossRefGoogle Scholar
  22. Lazos D, Sproul AB, Kay M (2014). Optimisation of energy management in commercial buildings with weather forecasting inputs: A review. Renewable and Sustainable Energy Reviews, 39: 587–603.CrossRefGoogle Scholar
  23. Li K, Pan L, Xue W, Jiang H, Mao H (2017). Multi-objective optimization for energy performance improvement of residential buildings: A comparative study. Energies, 10(2): 245.CrossRefGoogle Scholar
  24. Lin S-W, Ying K-C, Chen S-C, Lee Z-J (2008). Particle swarm optimization for parameter determination and feature selection of support vector machines. Expert Systems with Applications, 35: 1817–1824.CrossRefGoogle Scholar
  25. Mahmood D, Javaid N, Alrajeh N, Khan Z, Qasim U, Ahmed I, Ilahi M (2016). Realistic scheduling mechanism for smart homes. Energies, 9(3): 202.CrossRefGoogle Scholar
  26. Makonin S, Popowich F, Bartram L, Gill B, Bajic IV (2013). AMPds: A public dataset for load disaggregation and eco-feedback research. In: Proceedings of IEEE Electrical Power & Energy Conference, Halifax, Canada.Google Scholar
  27. Makonin S, Ellert B, Bajić IV, Popowich F (2016). Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Scientific Data, 3: 160037.CrossRefGoogle Scholar
  28. Muralitharan K, Sakthivel R, Vishnuvarthan R (2018). Neural network based optimization approach for energy demand prediction in smart grid. Neurocomputing, 273: 199–208.CrossRefGoogle Scholar
  29. Rasheed M, Javaid N, Ahmad A, Jamil M, Khan Z, Qasim U, Alrajeh N (2016). Energy optimization in smart homes using customer preference and dynamic pricing. Energies, 9(8): 593.CrossRefGoogle Scholar
  30. Sharma N, Sharma P, Irwin D, Shenoy P (2011). Predicting solar generation from weather forecasts using machine learning. In: Proceedings of IEEE International Conference on Smart Grid Communications (SmartGridComm), Brussels, Belgium.CrossRefGoogle Scholar
  31. Singh S, Yassine A (2017). Mining energy consumption behavior patterns for households in smart grid. IEEE Transactions on Emerging Topics in Computing,
  32. Subbiah R, Pal A, Nordberg EK, Marathe A, Marathe MV (2017). Energy demand model for residential sector: A first principles approach. IEEE Transactions on Sustainable Energy, 8: 1215–1224.CrossRefGoogle Scholar
  33. Swan LG, Ugursal VI (2009). Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. Renewable and Sustainable Energy Reviews, 13: 1819–1835.CrossRefGoogle Scholar
  34. Tian W, Song J, Li Z (2014). Spatial regression analysis of domestic energy in urban areas. Energy, 76: 629–640.CrossRefGoogle Scholar
  35. Torabi M, Hashemi S, Saybani MR, Shamshirband S, Mosavi A (2019). A hybrid clustering and classification technique for forecasting short-term energy consumption. Environmental Progress & Sustainable Energy, 38: 66–76.CrossRefGoogle Scholar
  36. Wang X, Palazoglu A, El-Farra N (2015). Operational optimization and demand response of hybrid renewable energy systems. Applied Energy, 143: 324–335.CrossRefGoogle Scholar
  37. Wang Z, Wang Y, Srinivasan RS (2018a). A novel ensemble learning approach to support building energy use prediction. Energy and Buildings, 159: 109–122.CrossRefGoogle Scholar
  38. Wang Z, Wang Y, Zeng R, Srinivasan RS, Ahrentzen S (2018b). Random forest based hourly building energy prediction. Energy and Buildings, 171: 11–25.CrossRefGoogle Scholar
  39. Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, Han M, Zhao X (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82: 1027–1047.CrossRefGoogle Scholar
  40. Wood SN (2001). mgcv: GAMS and generalized ridge regression for R. R News, 1(2): 20–25.Google Scholar
  41. Yin P-Y, Chao C-H (2018). Automatic selection of fittest energy demand predictors based on cyber swarm optimization and reinforcement learning. Applied Soft Computing, 71: 152–164.CrossRefGoogle Scholar
  42. Zhu G, Chow T-T, Tse N (2018). Short-term load forecasting coupled with weather profile generation methodology. Building Services Engineering Research and Technology, 39: 310–327.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia

Personalised recommendations