- Title
- Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data
- Creator
- Adedeji, Paul A, Akinlabi, Stephen, Madushele, Nkosinathi, Olatunji, Obafemi
- Subject
- ANFIS, Electricity Consumption, FCM
- Date
- 2019
- Type
- Conference proceedings
- Identifier
- http://hdl.handle.net/10210/406893
- Identifier
- uj:34228
- Identifier
- Citation: Adedeji, P.A. et al. 2019 : Neuro-fuzzy mid-term forecasting of electricity consumption using meteorological data.
- Description
- Abstract : Forecasting energy consumption is highly essential for strategic and operational planning. This study uses the Adaptive-Neuro-Fuzzy Inference System (ANFIS) for a mid-term 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.
- Language
- English
- Rights
- @2019, authors
- Full Text
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| Thumbnail | File | Description | Size | Format | |||
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| View Details Download | SOURCE1 | 14630_Manuscript CE 2019.pdf | 293 KB | Adobe Acrobat PDF | View Details Download |