Hybrid adaptive neuro-fuzzy inference system (ANFIS) for a multi-campus university energy consumption forecast

This study compares the performance of standalone adaptive neuro-fuzzy inference system (ANFIS) and its hybrid with particle swarm optimisation (PSO) in predicting the energy consumption from climatic factors for a multi-campus institution in South Africa. Monthly weather condition datasets (average wind speed, average maximum temperature, average minimum temperature, average dew point and average relative humidity) for 36 months were mapped with the corresponding monthly energy consumption for each campus as the model inputs and output respectively. The ANFIS and ANFIS-PSO models were trained and tested with 70% and 30% of the dataset respectively. The root mean square error (RMSE), mean absolute deviation (MAD), mean absolute percentage error (MAPE) and the computational time were used to evaluate the model for the four campuses. The ANFIS standalone model developed for campus C outperforms other standalone models for campus A, B and D with the following performance indices: RMSE = 1.27, MAD = 1.01, MAPE = 13.34, computational time = 14.61 secs. On the contrary, the ANFIS-PSO model developed for campus D outperforms both the standalone and the hybrid models for campus A, B and C with the following values of performance indices except for computational time: RMSE = 0.147, MAD = 0.125, MAPE = 2.89, computational time = 95.60 secs. This study concludes that tuning ANFIS parameters with PSO offers a better prediction accuracy, which is reliable for strategic energy planning, though at a higher computational time.