Impact of Sustainable Development and Environmental Protection on the Volume of Domestic Hard Coal Sales in Poland

The paper presents the results of an analysis of the impact of activities related to the implementation of Poland’s climate policy on the level of demand for hard coal. The authors used sets of indicators built by Eurostat during the analysis. The analysis was based on a set of indicators that had not previously been used for this purpose. The applied ARAMAX model made it possible to study the impact of the presented indicators on the volume of demand for hard coal in Poland. They were introduced to the ARMAX model as explanatory variables. The demand for hard coal in Poland was the dependent variable. The set of indicators was verified, and finally only statistically significant factors were used to build the model. The forecast of the demand for hard coal was made until 2022. It showed that the volume of coal sales would systematically fall as long as factors affecting demand remain constant. However, an additional factor was taken into account in the analysis, namely the increase in demand and prices for hard coal on world markets. The ARIMA model was used to forecast price levels for the next 12 months. The forecast indicates that the time series of prices should maintain an upward trend within the examined time period. Building an accurate and reliable forecast is the basis for effective planning of coal production and is adjusted to the demand for this fuel.

Input Excitation Analysis for Black-Box Quadrotor Model System Identification

System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.

Machine Learning in Internet Search Query Selection for Tourism Forecasting

Prior studies have shown that Internet search query data have great potential to improve tourism forecasting. As such, selecting the most relevant information from large amounts of search query data is crucial to enhancing forecasting accuracy and reducing overfitting; however, such feature selection methods have not been considered in the tourism forecasting literature. This study employs four machine learning–based feature selection methods to extract useful search query data and construct relevant econometric models. We examined the proposed methods based on monthly forecasting of tourist arrivals in Beijing, China, along with weekly forecasting of hotel occupancy in the city of Charleston, South Carolina, USA. Our findings indicate that the forecasting model with the selected search keywords outperformed the benchmark ARMAX model without feature selection in forecasting tourism demand and hotel occupancy. Therefore, machine learning methods can identify the most useful search query data to significantly improve forecasting accuracy in tourism and hospitality.

Forecasting hydrologic parameters using linear and nonlinear stochastic models

One of the most important bases in the management of catchments and sustainable use of water resources is the prediction of hydrological parameters. In this study, support vector machine (SVM), support vector machine combined with wavelet transform (W-SVM), autoregressive moving average with exogenous variable (ARMAX) model, and autoregressive integrated moving average (ARIMA) models were used to predict monthly values of precipitation, discharge, and evaporation. For this purpose, the monthly time series of rain-gauge, hydrometric, and evaporation-gauge stations located in the catchment area of Hamedan during a 25-year period (1991–2015) were used. Out of this statistical period, 17 years (1991–2007), 4 years (2008–2011), and 4 years (2012–2015) were used for training, calibration, and validation of the models, respectively. The results showed that the ARIMA, SVM, ARMAX, and W-SVM ranked from first to fourth in the monthly precipitation prediction and SVM, ARIMA, ARMAX, and W-SVM were ranked from first to fourth in the monthly discharge and monthly evaporation prediction. It can be said that the SVM has fewer adjustable parameters than other models. Thus, the model is able to predict hydrological changes with greater ease and in less time, because of which it is preferred to other methods.

Auto-regressive moving average with exogenous excitation model based experimental identification and optimal discrete multi-poles shifting control of a flexible piezoelectric manipulator

Auto-regressive moving average with exogenous excitation (ARMAX) model based experimental identification and vibration suppression of a flexible piezoelectric manipulator are conducted. Experimental identification based on ARMAX models with different orders is conducted. To remove the spurious modes that do not correspond to structural modes, a balanced model reduction method based on the Hankel singular value is employed. After balanced transformation and model reduction, the identified ARMAX model with a high order is transformed into a reduced-order controllable and observable system. Comparative experimental results show that the identified reduced-order model matches closely with the system dynamics. Therefore, an optimal discrete multi-poles shifting controller, which combines the multi-poles recursive shifting method and linear quadratic regulator (LQR) control, is proposed. The first complex conjugate poles pair of the system is shifted to some desired positions by solving a discrete algebraic equation. The weighting matrixes of the LQR are determined based on the solution of the discrete algebraic equation. Then, multi-poles of the identified model are shifted through recursive applications of the reduced-order poles shifting method. Experimental results show that vibrations of the manipulator are significantly diminished. Consequently, the effectiveness of the proposed controller is proven.