Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

Data-intelligent algorithms designed for forecasting significant height of coastal waves over the relatively short time period in coastal zones can generate crucial information about enhancing the renewable energy production. In this study, a machine learning model is designed and evaluated for forecasting significant wave height (Hs) within the eastern coastal zones of Australia. The extreme learning machine (ELM) model is coupled with an improved complete ensemble empirical mode decomposition method with adaptive noise (ICEEMDAN) to design the proposed ICEEMDAN-ELM model. This model incorporates the historical lagged series of Hs as the model's predictor to forecast future Hs. The ICEEMDAN algorithm demarcates the original Hs data from January-2000 to March-2018, recorded at 30-min intervals, into decomposed signals i.e., intrinsic mode functions (IMFs) and a residual component. After decomposition, the partial autocorrelation function is determined for each IMF and the residual sub-series to determine the statistically significant lagged input dataset. The ELM model is applied for forecasting of each IMF by incorporating the significant antecedent Hs sub-series as inputs. Finally, all the forecasted IMFs are summed up to obtain the final forecasted Hs. The results are benchmarked with those from an online sequential extreme learning machine (OSELM) and random forest (RF) integrated with ICEEMDAN, i.e., the ICEEMDAN-OSELM and ICEEMDAN-RF models. The proposed ICEEMDAN-ELM model is tested geographically at two coastal sites of the Queensland state, Australia. The testing performance of all the standalone (ELM, OSELM, RF) and integrated models (ICEEMDAN-ELM, ICEEMDAN-OSELM, ICEEMDAN-RF), according to robust statistical error metrics, is satisfactory; however, the hybrid ICEEMDAN-ELM model is found to be a beneficial Hs forecasting tool in accordance to high performance accuracy. The proposed ICEEMDAN-ELM model can be considered as a pertinent decision-support framework and is vital for designing of reliable ocean energy converters.

Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition

A hybrid EMD-SVR model for the short-term prediction of significant wave height

Forecasting short-term electricity load using hybrid support vector regression with grey catastrophe and random forest modeling

The accurate prediction of global solar radiation (GSR) with remote sensing in metropolitan, regional and remote, yet solar-rich sites, is a core requisite for cleaner energy utilization, monitoring and conversion of renewable energy into usable power. Data-driven models that investigate the feasibility of solar-fueled energies, face challenges in respect to identifying their appropriate input data as such variables may not be available at all sites due to a lack of environmental monitoring system. In this paper, the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite-derived predictors are employed to train three-phase hybrid SVR model for monthly GSR prediction. Firstly, to acquire relevant model input features, MODIS variables are screened with the Particle Swarm Optimization (PSO) algorithm, and secondly, a Gaussian emulation method of sensitivity analysis is incorporated on all screened variables to ascertain their relative role in predicting GSR. To address pertinent issues of non-stationarities, PSO selected variables are decomposed with Maximum Overlap Discrete Wavelet Transformation prior to its incorporation in Support Vector Regression (SVR), constructing a three-phase PSO-W-SVR hybrid model where the hyper-parameters are acquired by evolutionary (i.e., PSO & Genetic Algorithm) and Grid Search methods. Three-phase PSO-W-SVR hybrid model is benchmarked with alternative machine learning models. Thirty-nine model scenarios are formulated: 13 without feature selection (e.g., SVR), 13 with feature selection (e.g., PSO-SVR for two-phase models) and the remainder 13 with feature selection strategy coupled with data decomposition algorithm (e.g., PSO-W-SVR leading to a three-phase model). Metrics such as skill score (RMSESS), root mean square error (RMSE), mean absolute error (MAE), Willmott’s (WI), Legates & McCabe’s ( E 1 ) and Nash–Sutcliffe coefficients ( E N S ) are applied to comprehensively evaluate prescribed models. Empirical results register high performance of three-phase hybrid PSO-W-SVR models, exceeding the prescribed alternative models. High predictive ability evidenced by a low RRMSE and high E1 ascertains PSO-W-SVR hybrid model as considerably favorable in its capability to be enriched by MODIS satellite-derived variables. Maximum Overlap Discrete Wavelet Transform algorithm is also seen to provide resolved patterns in satellite variables, leading to a superior performance compared to the other data-driven model. The research avers that a three-phase hybrid PSO-W-SVR model can be a viable tool to predict GSR using satellite derived data as predictors, and is particularly useful for exploration of renewable energies where satellite footprint are present but regular environmental monitoring systems may be absent.

Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction

Accurate and reliable short-term prediction of ship motions offers improvements in both safety and control quality in ship motion sensitive maritime operations. Inspired by the satisfactory nonlinear learning capability of a support vector regression (SVR) model and the strong non-stationary processing ability of empirical mode decomposition (EMD), this paper develops a hybrid autoregressive (AR)-EMD-SVR model for the short-term forecast of nonlinear and non-stationary ship motion. The proposed hybrid model is designed by coupling the SVR model with an AR-EMD technique, which employs an AR model in ends extension. In addition to the AR-EMD-SVR model, the linear AR model, non-linear SVR model, and hybrid EMD-AR model are also studied for comparison by using ship motion time series obtained from model testing in a towing tank. Prediction results suggest that the non-stationary difficulty in the SVR model is overcome by using the AR-EMD technique, and better predictions are obtained by the proposed AR-EMD-SVR model than other models.

https://link.springer.com/content/pdf/10.1631%2Fjzus.A1500040.pdf

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. Autoregressive (AR) model is an efficient and highly adaptive approach for wave forecasting. However, it is based on linear and stationary theory and hence has limitations in forecasting nonlinear and non-stationary waves. Inspired by the capability of empirical mode decomposition (EMD) technique in handling nonlinear and non-stationary signals, this paper describes the development of a hybrid EMD-AR model for nonlinear and non-stationary wave forecasting. The EMDAR model was developed by coupling an AR model with the EMD technique. Nonlinearity and non-stationarity were overcome by decomposing the wave time series into several simple components for which the AR model is suitable. The EMD-AR model was implemented using measured significant wave height data from the National Data Buoy Center, USA. Prediction results from various locations consistently show that the hybrid EMD-AR model is superior to the AR model. This demonstrates that the EMD technique is effective in processing nonlinear and non-stationary waves.

/pdf/a-hybrid-emd-ar-model-for-nonlinear-and-non-stationary-wave-b1k2mvem1x.pdf

W.Y. Duan

Papers

A hybrid EMD-SVR model for the short-term prediction of significant wave height

A hybrid AR-EMD-SVR model for the short-term prediction of nonlinear and non-stationary ship motion

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

Experimental and numerical study of the water entry of projectiles at high oblique entry speed

Hydrodynamic analysis of floating breakwater with perforated structure based on the Taylor expansion boundary element method