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Forecasting electric demand of supply fan using data mining techniques

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  • Le Cam, M.
  • Daoud, A.
  • Zmeureanu, R.

Abstract

This paper presents the application of the process of KDD (knowledge discovery in databases) for the forecasting of the electrical power demand of a supply fan of an AHU (air handling unit). The case study uses trend data from the BAS (Building Automation System), which is recorded every 15 min in an office building. Data mining techniques are used as a preprocessing step in the development of the forecasting model. A clustering analysis detects atypical operations and then partitions the whole dataset into three subsets of typical daily profiles of the supply fan modulation. A hybrid model, combining a closed-loop nonlinear ANN (autoregressive neural network) model and a physical model, forecasts the electric power demand over a horizon of up to 6 h. The optimum architecture of ANN, found by using a Simple Genetic Algorithm, is composed of 13 input neurons, 1 hidden neuron and 23-day training set size, for the cluster corresponding to working days except Mondays. The results show good agreement between the forecasts and measurements of fan modulation, and electric demand, respectively. The fan modulation was forecasted over the testing period with RMSE (Root Mean Squared Error) of 5.5% and CV(RMSE) of 17.6%. The fan electric demand was forecasted with a RMSE of 1.4 kW, CV(RMSE) of 30% over a 6-h time horizon. The sensitivity analysis indicated that the reduction of training data set size from 23 days to 4 or 8 days does not have a negative impact of the value of RMSE.

Suggested Citation

  • Le Cam, M. & Daoud, A. & Zmeureanu, R., 2016. "Forecasting electric demand of supply fan using data mining techniques," Energy, Elsevier, vol. 101(C), pages 541-557.
    • Handle: RePEc:eee:energy:v:101:y:2016:i:c:p:541-557
    DOI: 10.1016/j.energy.2016.02.061
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    Cited by:

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    3. Jee-Heon Kim & Nam-Chul Seong & Wonchang Choi, 2020. "Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models," Energies, MDPI, vol. 13(17), pages 1-12, August.
    4. Jinchai Lin & Kaiwei Zhu & Zhen Liu & Jenny Lieu & Xianchun Tan, 2019. "Study on A Simple Model to Forecast the Electricity Demand under China’s New Normal Situation," Energies, MDPI, vol. 12(11), pages 1-28, June.
    5. Le Cam, M. & Zmeureanu, R. & Daoud, A., 2017. "Cascade-based short-term forecasting method of the electric demand of HVAC system," Energy, Elsevier, vol. 119(C), pages 1098-1107.
    6. Bismark Ameyaw & Li Yao, 2018. "Sectoral Energy Demand Forecasting under an Assumption-Free Data-Driven Technique," Sustainability, MDPI, vol. 10(7), pages 1-20, July.
    7. Jason Runge & Radu Zmeureanu, 2019. "Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review," Energies, MDPI, vol. 12(17), pages 1-27, August.

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