Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data. (September 2019)

• Record Type: Journal Article
• Authors: Adedeji, Paul A.; Akinlabi, Stephen; Madushele, Nkosinathi; Olatunji, Obafemi
• Is Part Of: IOP conference series. Volume 331(2019)
• Publication Date: 2019-09
• Subjects: ANFIS -- Electricity Consumption -- FCM -- GP -- Mid-term Forecasting; Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/331/1/012017
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: 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.