Search

Wind speed oscillations are known to exhibit varying characteristics at different time scales. Our recent analysis has shown that a collection of autoregressive models fitted separately on the frequency components of wind speed data can significantly increase the prediction accuracy. In this paper, we report the results of the investigation of dynamical behaviour across a broad frequency spectrum of wind speed measurements. The results show the existence of diverse characteristics such as stochastic, deterministic and chaotic behaviour apart from the variation of the dimensionality of underlying dynamics as well as the degree of fluctuations. It is also demonstrated that a cluster of deterministic models built upon separate frequency components of a wind speed time series can enhance the prediction accuracy by as much as 80%, on the average, consistently for predictions up to 12 h. The comparison shows the definite advantage of deterministic prediction models over autoregressive models. The f-index introduced in this paper to measure the fluctuations of wind speed over a period indicates that the observed seasonal variations of prediction errors can be correlated with changes in the f-index of the component series contributed mostly by the lower scales of decomposition. [ABSTRACT FROM AUTHOR]

Simple linear methods are widely used for time series modelling and prediction and in particular for the forecast of wind speed variations. Linear prediction models are popular for their simplicity and computational efficiency, but their prediction accuracy generally deteriorates beyond a few time steps. In this paper we demonstrate that the prediction accuracy of simple auto-regressive (AR) models can be significantly improved, by as much as 60.15% for day-ahead predictions and up to 18.25% for week-ahead predictions, when combined with suitable time series decomposition. The comparison with new reference forecast model (NRFM) also shows similar accuracy gain of week ahead predictions. The combined model is capable of forecasting wind speed up to 7 days ahead with an average root mean square error less than 3 m/s. We also compare the performance of AR and f-ARIMA models in wind speed prediction and observe that the f-ARIMA model is no better than the AR model when used in combination with time series decomposition. [ABSTRACT FROM AUTHOR]

• First report of investigation of molecular dynamics in a thermal lens system by Time series analysis. • Investigation of the effect of duty cycle on molecular dynamics. • The complexity mapping in TL system with changing duty cycle is studied by phase portrait and fractal analysis. • The Hurst exponent reveals the antipersistent nature of the system. • The study correlates sample entropy with thermodynamic entropy. The present work employs time series analysis, a proven powerful mathematical tool, for investigating the complex molecular dynamics of the thermal lens (TL) system induced by the duty cycle (C) variation. For intensity modulation, TL spectroscopy commonly uses optical choppers. The TL formation involves complex molecular dynamics that vary with the input photothermal energy, which is implemented by varying the duty cycle of the chopper. The molecular dynamics is studied from the fractal dimension (D), phase portrait, sample entropy (S), and Hurst exponent (H) for different duty cycles. The increasing value of C is found to increase D and S, indicating that the system is becoming complex and less deterministic, as evidenced by the phase portrait analysis. The value of H less than 0.5 conforms the evolution of the TL system to more antipersistent nature with C. The increasing value of C increases the enthalpy of the system that appears as an increase in full width at half maximum of the refractive index profile. Thus the study establishes that the sample entropy and thermodynamic entropy are directly related. [ABSTRACT FROM AUTHOR]

The gamut of geothermal energy research encompasses the studies aimed at harnessing the abundant and inexhaustible thermal energy within the Earth, and it ranges from heat transfer to the activity of thermophilic microorganisms, 3D printing, and additive manufacturing and impacts the NET ZERO endeavour of humanity. In this paper, computational social network analysis has been employed to discover the subfield clusters of geothermal energy research and further trace the key evolutionary routes from the research corpus. The development, limitations, and opportunities of each cluster are examined, and it becomes evident that the focus of research ranges from geothermal evaluation, long-term effects of borehole heat exchangers, shallow systems that employ urbanisation's ground heating, enhanced geothermal systems (EGS) for district heating, combined and hybridised geothermal power generating models, including multi-generation and poly-generation, geothermal fluids, reinjection and their dual nature, environmental effects in geothermal water and mineral scaling, enhanced geothermal systems aiming to increase permeability without causing seismicity, and finally to social acceptability. We address significant questions, such as whether the waste heat is compatible with the idea of green geothermal heat and the elimination of pollutants and find that further R&D and technological advancements are required for this ubiquitous clean energy to get wider acceptance and employment. The future of this energy depends on the rational and scientifically sound exploration and use of the resources, just as in the case of fossil fuels, and thus precludes geothermal energy as a win-all solution to the energy needs of the whole world. • Computational analysis of geothermal energy research themes and linkages. • Algorithmic theme identification rather than expert-based analysis. • Tracing the key-routes of domain evolution. • Subdomain-specific status, limits, emerging fronts and future research agenda. • Impact analysis of contemporary technologies such as AI and 3D printing. [ABSTRACT FROM AUTHOR]

• New method for wind speed extrapolation to higher altitude using shorter met masts. • Data-driven improved modeling in lieu of empirical equations. • Only 1-day data at 10-min intervals is enough to generate the best fitting symbolic function. • Maximum of 61.04% reduction achieved in daily Root Mean Square Error with respect to power law. • Superiority in performance observed always up to 34 km from the reference mast. Prolonged wind speed assessment at higher altitudes is essential for wind energy estimation and planning. However, the erection and maintenance of tall wind measuring masts for this purpose cause many practical inconveniences from the engineering perspective. A rather simplified method often used for this task is to measure the wind profile at relatively lower altitudes and extrapolate the same to the required higher heights by empirical equations framed using hypothetical and experiential research. Such models often show errors due to the uncertainties caused by the complex nature of turbulent flows and the terrain. In this paper, we propose a new method of applying symbolic regression to the wind speed data over a short duration measured at a reference location to obtain a symbolic function capable of estimating wind speeds at higher altitudes using wind speed data at lower altitudes at different locations. Compared to the traditional power law method, the new method performs more accurately in different seasons at the reference as well as far away locations, achieving a maximum of 61.04% reduction in daily RMSE when analyzed with wind speeds averaged over 10-min intervals in this study. The new method opens up the possibility of wind resource assessment at higher altitudes at different locations by employing engineering-friendly shorter wind measuring masts. [ABSTRACT FROM AUTHOR]