Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data

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6 Citations (Scopus)


Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a midterm forecast of electricity consumption. The model comprises of three meteorological variables as inputs and electricity consumption as output. Two ANFIS models with two clustering techniques (Fuzzy c-Means (FCM) and Grid Partitioning (GP) were developed (ANFIS-FCM and ANFIS-GP) to forecast monthly energy consumption based on meteorological variables. The performance of each model was determined using known statistical metrics. This compares the predicted electricity consumption with the observed and a statistical significance between the two reported. ANFIS-FCM model recorded a better mean absolute deviation (MAD), root mean square (RMSE), and mean absolute percentage error (MAPE) values of 0.396, 0.738, and 8.613 respectively compared to the ANFIS-GP model, which has MAD, RMSE, and MAPE values of 0.450, 0.762, and 9.430 values respectively. The study established that FCM is a good clustering technique in ANFIS compared to GP and recommended a comparison between the two techniques on hybrid ANFIS model.

Original languageEnglish
Article number012017
JournalIOP Conference Series: Earth and Environmental Science
Issue number1
Publication statusPublished - 16 Oct 2019
Event1st International Conference on Energy and Sustainable Environment, ICESE 2019 - Ota, Nigeria
Duration: 18 Jun 201920 Jun 2019


  • Electricity Consumption
  • FCM
  • GP
  • Mid-term Forecasting

ASJC Scopus subject areas

  • General Environmental Science
  • General Earth and Planetary Sciences


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