Machine Intelligent Hybrid Methods Based on Kalman Filter and Wavelet Transform for Short-Term Wind Speed Prediction

Wind power’s increasing penetration into the electricity grid poses several challenges for power system operators, primarily due to variability and unpredictability. Highly accurate wind predictions are needed to address this concern. Therefore, the performance of hybrid forecasting approaches combining autoregressive integrated moving average (ARIMA), machine learning models (SVR, RF), wavelet transform (WT), and Kalman filter (KF) techniques is essential to examine. Comparing the proposed hybrid methods with available state-of-the-art algorithms shows that the proposed approach provides more accurate prediction results. The best model is a hybrid of KF-WT-ML with an average R2 score of 0.99967 and RMSE of 0.03874, followed by ARIMA-WT-ML with an average R2 of 0.99796 and RMSE of 0.05863 over different datasets. Moreover, the KF-WT-ML model evaluated on different terrains, including offshore and hilly regions, reveals that the proposed KF based hybrid provides accurate wind speed forecasts for both onshore and offshore wind data.

A Novel Hybridization of ARIMA, ANN, and K-Means for Time Series Forecasting

This article aims to propose a novel hybrid forecasting model involving autoregressive integrated moving average (ARIMA), artificial neural networks (ANNs) and k-means clustering. The single models and k-means clustering are used to build the hybrid forecasting models in different levels of complexity (i.e. ARIMA; hybrid model of ARIMA and ANNs; and hybrid model of k-means, ARIMA, and ANN). To obtain the final forecasting value, the forecasted values of these three models are combined with the weights generated from the discount mean square forecast error (DMSFE) method. The proposed model is applied to three well-known data sets: Wolf's sunspot, Canadian lynx and the exchange rate (British pound to US dollar) to evaluate the prediction capability in three measures (i.e. MSE, MAE, and MAPE). In addition, the prediction performance of the proposed model is compared to ARIMA; ANNs; Khashei and Bijari's model; and the hybrid model of k-means, ARIMA, and ANN. The obtained results show that the proposed model gives the best performance in MSE, MAE, and MAPE for all three data sets.

Forecasting carbon futures price: a hybrid method incorporating fuzzy entropy and extreme learning machine

In this paper, we propose a novel hybrid model that extends prior work involving ensemble empirical mode decomposition (EEMD) by using fuzzy entropy and extreme learning machine (ELM) methods. We demonstrate this 3-stage model by applying it to forecast carbon futures prices which are characterized by chaos and complexity. First, we employ the EEMD method to decompose carbon futures prices into a couple of intrinsic mode functions (IMFs) and one residue. Second, the fuzzy entropy and K-means clustering methods are used to reconstruct the IMFs and the residue to obtain three reconstructed components, specifically a high frequency series, a low frequency series, and a trend series. Third, the ARMA model is implemented for the stationary high and low frequency series, while the extreme learning machine (ELM) model is utilized for the non-stationary trend series. Finally, all the component forecasts are aggregated to form final forecasts of the carbon price for each model. The empirical results show that the proposed reconstruction algorithm can bring more than 40% improvement in prediction accuracy compared to the traditional fine-to-coarse reconstruction algorithm under the same forecasting framework. The hybrid forecasting model proposed in this paper also well captures the direction of the price changes, with strong and robust forecasting ability, which is significantly better than the single forecasting models and the other hybrid forecasting models.

A hybrid forecasting model for ageing breast deformation during yoga practice

In this work, the deformation of bilateral breasts was investigated with an established hybrid model to predict the nipple movement specifically for senior women during yoga exercise. A motion capture system was used to collect the displacement of 10 markers on the breasts from 11 senior women (average age of 62) during yoga practice and then the data were analyzed by integrating the absolute grey relation analysis (AGRA) and extreme learning machine (ELM). The right and left breasts had the maximum motion amplitude in the horizontal direction but they were respectively featured with contraction and extension during yoga practice. AGRA showed that the nipple motion was highly associated with the vertical region above the nipple for the left breast but the parallel region along with the nipple for the right breast. The ELM model is able to predict the nipple movement within tolerable error (∼0.0037). This study lays a foundation for a better understanding of ageing breast kinematics during yoga poses with limited practical experiments. Besides, the accurate and efficient results can be used not only for yoga pose instruction but also for ergonomic sports bra design.

Efficient Crude Oil Pricing Using a Machine Learning Approach

Crude oil, a base for more than 6000 products that we use on a daily basis, accounts for 33% of global energy consumption. However, the outbreak and transmission of COVID-19 had significant implications for the entire value chain in the oil industry. The price crash and the fluctuations in price is known to have far reaching effect on global economies, with Nigeria hard. It has therefore become imperative to develop a tool for forecasting the price of crude oil in order to minimise the risks associated with volatility in oil prices and also be able to do proper planning. Hence, this article proposed a hybrid forecasting model involving a classical and machine learning techniques – autoregressive neural network, in determining the prices of crude oil. The monthly data used were obtained from the Central Bank of Nigeria website, spanning January 2006 to October 2020. Statistical efficiency was computed for the hybrid, and the models from which the proposed hybrid was built, using the percent relative efficiency. Analyses showed that the efficiency of the hybrid model, at 20 and 100 hidden neurons, was higher than that of the individual models, the latter being the best performing. The study recommends urgent diversification of the economy in order not for the nation to be plunged into a seemingly unending recession.