Solar power forecasting using wavelet transform and machine learning approaches / Nor Azliana Abdullah

Nor Azliana , Abdullah (2020) Solar power forecasting using wavelet transform and machine learning approaches / Nor Azliana Abdullah. Masters thesis, University of Malaya.

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      Generation of photovoltaic (PV) power is intermittent in nature and integration of PV system into the grid system causes an imbalanced power production and power demand. One of the efforts to reduce this problem is to forecast the generation of solar power in the PV system. Solar power forecasting requires the collection of solar power and meteorological data. Hence, this work collected solar power data and various meteorological data (global radiation, tilted radiation, temperature surrounding, humidity surrounding, PV module/ PV panel temperature and wind speed) from Universiti Teknikal Malaysia Melaka (UTeM). A pre-processing process is carried out to ensure that solar power data and meteorological data can be simplified. The proposed work of this study is divided into four phases of works. The work in Phase 1 presents the solar power data and meteorological data into three forecasting models such as Multi-Layer Perceptron (MLP), Radial Basis Function Neural Network (RBFNN) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The performance of every forecasting model is estimated. The work in Phase 2 proposes a Wavelet Transform (WT) technique to remove noise in solar power data and meteorological data. The existence of noise in data is due to the presence of dirt on the sensor of measurement. The denoised solar power and meteorological data are then presented to MLP, RBFNN and ANFIS to conduct the forecasting process. The performance of MLP, RBFNN and ANFIS in Phase 1 and Phase 2 are compared. The comparison result is presented in Phase 3 to estimate the efficiency usage of WT to eliminate noise. The result in Phase 3 depicts an improved performance of MLP, RBFFN and ANFIS when employing WT technique. This can be proven when the values of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for MLP, RBFNN and ANFIS in Phase 2 are smaller than the values of MAE and RMSE in Phase 1. Apart from that, the Correlation of Coefficient (R) values for MLP=0.9793, RBFNN=0.9788 and ANFIS=0.9799 in Phase 2 are greater than the R-values of MLP=0.9709, RBFNN=0.9722 and ANFIS=0.9674 in Phase 1. The work in Phase 3 also selects the most accurate forecasting model based on the values of MAE, RMSE and R depicted by MLP, RBFNN and ANFIS in Phase 1 and Phase 2. The result of this work proves that the integration of WT with the ANFIS (WT-ANFIS) surpasses the performance of other forecasting models by providing the lowest MAE value of 0.0278 and lowest RMSE value of 0.0385. The work in the final phase which is Phase 4 includes the integration of Hybrid Firefly and Particle Swarm Optimisation (HFPSO) to optimise the premise parameters of WT-ANFIS. It is observed from the result of WT-ANFIS-HFPSO that the Mean Square Error (MSE) value of 0.0012175, RMSE value of 0.034892 and MAE value of 0.025361 are the lowest compared to the integration of WT-ANFIS with single Firefly (WT-ANFIS-FF) and single Particle Swarm Optimisation (WT-ANFIS-PSO). Furthermore, the WT-ANFIS-HFPSO presents the R-value of 0.98220 which indicates the capability of the model to follow the data pattern efficiently. From the comparative analysis, WT-ANFIS-HFPSO has confirmed its reliability as a forecaster of solar power.

      Item Type: Thesis (Masters)
      Additional Information: Dissertation (M.A.) – Institute of Advanced Studies, University of Malaya, 2020.
      Uncontrolled Keywords: Adaptive neuro-fuzzy inference system; Firefly; Hybrid firefly; Particle swarm optimisation; Particle swarm optimisation; Wavelet transform
      Subjects: Q Science > Q Science (General)
      Q Science > QC Physics
      Divisions: Institute of Advanced Studies
      Depositing User: Mr Mohd Safri Tahir
      Date Deposited: 04 Mar 2021 08:11
      Last Modified: 04 Mar 2021 08:11

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