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Accurately forecasting energy demand can better predict future changes in energy demand. This study is aimed to develop more accurate clean energy prediction model from two perspectives of the modeling mechanisms and improvements on the model structure. For this purpose, a novel Riccati–Bernoulli differential equation is established through social practice theory, supply–demand relationship, and the preference of consumption in the energy economy. Considering the limited information in observation data, this differential equation is then transformed into a grey Riccati–Bernoulli model (GRBM(1,1)) according to the differential information principle. With the response function solved on the basis of polynomial equation theory and the power exponent optimized combining the modified flower pollination algorithm, the main process of GRBM(1,1) can be summarized. The four validation examples are provided for confirming the effectiveness and reliability of the new model by comparing with other existing models. Finally, the proposed model is employed to estimate and forecast the clean energy consumption in China and India. The results show that the proposed model demonstrates better estimation in all cases and efficiency in short-term clean energy consumption forecasting. Therefore, by using this optimum model, China's preference coefficient in total energy consumption is 0.7103, lower than that of India (0.8799), and China's preference coefficient in clean energy consumption is 0.9665, higher than that of Indian (0.7155), which means China has less interest to increase total energy consumption but more interest in popularizing the clean energy. • This paper proposed a novel grey Riccati–Bernoulli model for energy consumption prediction. • A specific solution algorithm for this Riccati–Bernoulli equation is discussed in this research. • We compared the proposed novel grey model with other 12 exiting prediction models. • The novel model also reveals China's effort in popularizing the use of clean energy. [ABSTRACT FROM AUTHOR]

Time-series modeling forms an important area of research in geophysics. Time-series models can be linear, like linear state-space models or non-linear, like artificial neural networks. One way to judge the goodness of different models associated with a given time-series is to assess the prediction capabilities of these models. Some of the important techniques used in time-series forecasting are: (i) Minimum mean squared error (MMSE) forecast obtained using conditional means of ARIMA(p,d,q) models, (ii) Kalman filter approach and (iii) Artificial neural networks (ANN) approach. However, the wavelet-based versions of these techniques, respectively denoted as W-MMSE, W-Kalman and W-ANN, rather than the original techniques themselves, have been found to possess better capabilities in forecasting the highly nonlinear geophysical data. Using the original and predicted data, these observations have been validated by determining the RMSE (root mean squared error) and correlation coefficients between them. The prediction capabilities of both versions of the above techniques are tested on (i) the ionospheric total electron content (TEC) data, (ii) the daily average rainfall data and (iii) the gamma-ray log data from an offshore oil well, off the west coast of India. The TEC and daily average rainfall data sets designate as examples of data with very high correlations pertaining over very large lags. They also have a strong seasonality component associated with them. However, the gamma-ray log data sets show no seasonality component and have no trends associated with them. Therefore, the choice of different nonlinear data sets having diverse sources of their origins are apt to test the forecasting capabilities of these techniques. It has been observed that W-ANN gives the best prediction, when compared with the other algorithms discussed in this paper. This is believed to be due to the use of non-linear activation function by ANNs to produce regression that results in capturing the inherent non-linear dynamics of the process effectively. The results also show the usefulness of discrete wavelet transform (DWT) coefficients as training features for both linear and non-linear forecasting approaches. The better performance of the wavelet-based forecasting algorithms can be attributed to the fact that DWT coefficients are wide-sense stationary sequences. Thus the wavelet-based versions of these models like W-MMSE and W-Kalman provide better fit to these coefficients, compared to the original time-series data itself. • Wavelet-based forecasting techniques, W-MMSE, W-Kalman and W-ANN are introduced. • W-MMSE, W-Kalman and W-ANN perform better than the regular known techniques. • W-MMSE, W-Kalman and W-ANN are tested on different nonlinear geophysical signals. • W-ANN produces best forecast compared to the other tested wavelet-based techniques. [ABSTRACT FROM AUTHOR]

Summary: This paper addresses the method of forecasting the wind and solar power and its application to an islanded microgrid (MG) model for load frequency control. Due to high penetration of renewable energy sources, the islanded MG suffers from lower equivalent inertia. The islanded MG faces several challenges in order to ensure the stable operation by maintaining the frequency and voltage at nominal value. The supply and demand power mismatch is mainly due to continuously changing solar irradiance, fluctuating wind speed, variable inertia, and load fluctuations. The intermittent nature of RESs can significantly affect the system stability; hence, the challenge lies in accurate forecasting of power from the renewable energy sources (RESs) so that a proactive arrangement is made available for compensation of active power or frequency variations. The forecasting will determine the correct estimate of power availability so that the power reserves can be activated prior to large variations in active power affecting the stability of the MGs. To address these challenges, a stochastic model of wind and solar has been developed using "Time series modeling" of the data obtained from Charanka Solar Park under Gujarat Energy Development Agency, India. Wind and solar power availability are forecasted using autoregressive integrated moving average (ARIMA) method including the seasonality factor. The proportional and integral (PI) controller is used for regulating the frequency fluctuations caused due to intermittency in the output of RESs and load power. Various load patterns are applied to the MG model to analyze its load frequency behavior along with variations in secondary sources. [ABSTRACT FROM AUTHOR]