## 1 Introduction
The adaptive neuro-fuzzy inference system (ANFIS) technique was applied by [PERSON] et al. to simulate solar radiation and the result described a better performance of ANFIS when compared with the persistence model, support vector regression (SVR) model and ANN model [14]. [PERSON] et al. applied the ANFIS and Autoregressive moving average with exogenous input (ARMAX) model to forecast the global solar radiation (GSR) in Nigeria [15]. This work verified the better performance of the ANFIS than the ARMAX in achieving a mean absolute percentage error (MAPE) value of less than 10% for the training process and testing process. [PERSON] et al. employed the ANFIS model to predict monthly solar radiation for 31 districts in Tamil Nadu, India and the proposed model demonstrated a better result when compared with other methods used by previous works [16]. A solar radiation forecasting model that is based on the support vector machine (SVM) was proposed by [PERSON] et al. [17]. Improved forecasting accuracy of the proposed model was obtained by comparing the SVM with the ANN model. [PERSON] et al. employed a least squares support vector machine (LS-SVM) to predict solar insolation on daily basis, by considering the number of days, daily mean temperature, daily maximum temperature, sunshine duration and solar insolation of the previous day as inputs of the developed model [18]. An LS-SVM model was also developed by [PERSON] et al. for short-term solar power prediction by employing two-dimensional (2D) transmissivity and meteorological variables (sky cover, relative humidity and wind speed) as the model inputs [19]. The proposed model was revealed to have outperformed the autoregressive (AR) and RBFNN models in terms of forecasting accuracy. There are some studies that incorporate a forecasting model with an optimization algorithm. [PERSON] et al. proposed a hybrid model of an ANN and particle swarm optimization (PSO) to estimate the monthly mean daily GSR [20]. Similarly, [PERSON] et al. [21] and [PERSON] et al. [22] applied a random forest-firefly algorithm and an SVM-firefly algorithm, respectively, for GSR prediction. All of the above-mentioned works of literature which combine a forecasting model with an optimization algorithm have given better accuracy during forecasting. However, those hybrid methods do not consider the data filtration technique used to eliminate noise in the data. The existence of noise in data has degraded the accuracy of a forecasting model for prediction. Due to this, many researchers have resorted to the use of a denoising technique to eliminate noise in time series data. [PERSON] et al. combined a wavelet transform (WT) and an ANN for wind power forecasting, where the WT was utilized as a denoising tool of the data [23]. Likewise, [PERSON] et al. applied a WT in filtering the noise of solar irradiance forecasting [24]. The existence of noise causes the data to become non-stationary, leading to the wrong model coefficient. As a result, the forecasting process becomes less accurate. By using a denoising tool, the data can be stationary and consequently improve the prediction accuracy of the model. In the literature from [PERSON] et al. [23] and [PERSON] et al. [24], only the denoising part was considered. The weaknesses of the ANN, such as the manual setting of parameters and local minima realization, were not mitigated. In fact, these weaknesses will reduce the predictive strength of hybrid models. To the best of the author's knowledge, the available publications focused on solar power forecasting under the following techniques:  
1. Incorporating a forecasting model with an optimization algorithm [20; 21; 22]. 2. Combining a denoising technique with a forecasting model [23; 24]. 3. Integrating a denoising technique with a forecasting model and a single optimization algorithm [5; 25].