Open AccessNeuro-fuzzy mid-term forecasting of electricity consumption using meteorological data
Author(s) -
Paul A. Adedeji,
Stephen A. Akinlabi,
Nkosinathi Madushele,
Obafemi O. Olatunji
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/331/1/012017
Subject(s) - adaptive neuro fuzzy inference system , mean absolute percentage error , mean squared error , statistics , cluster analysis , energy consumption , electricity , standard deviation , mathematics , computer science , fuzzy logic , environmental science , artificial intelligence , engineering , fuzzy control system , electrical engineering
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.