Iskanje po katalogu digitalne knjižnice

Opis: Climate change poses significant challenges for viticulture, particularly in regions known for producing high-quality wines. Wine quality results from a complex interaction between climatic factors, regional characteristics, and viticultural practices. Methods: This study integrates statistical analysis, machine learning (ML) algorithms, and systems thinking to assess the extent to which wine quality can be predicted using monthly weather data and regional classification. The dataset includes average wine scores, monthly temperatures and precipitation, and categorical region data for Slovenia between 2011 and 2021. Predictive models tested include Random Forest, Support Vector Machine, Decision Tree, and linear regression. In addition, Causal Loop Diagrams (CLDs) were constructed to explore feedback mechanisms and systemic dynamics. Results: The Random Forest model showed the highest prediction accuracy (R2 = 0.779). Regional classification emerged as the most influential variable, followed by temperatures in September and April. Precipitation did not have a statistically significant effect on wine ratings. CLD models revealed time delays in the effects of adaptation measures and highlighted the role of perceptual lags in growers’ responses to climate signals. Conclusions: The combined use of ML, statistical methods, and CLDs enhances understanding of how climate variability influences wine quality. This integrated approach offers practical insights for winegrowers, policymakers, and regional planners aiming to develop climate-resilient viticultural strategies. Future research should include phenological phase modeling and dynamic simulation to further improve predictive accuracy and system-level understanding.
Ključne besede: wine quality, machine learning, climate change, viticulture, Slovenia, terroir, statistical analysis, causal loop diagrams, system thinking
Objavljeno v DKUM: 18.08.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (2,51 MB)
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Opis: This Doctoral Thesis deals with the mechanisms for cross-border interchange and activation of the regulating reserves (RRs), i.e., Imbalance Netting Process (INP) and Cross-Border Activation of the RRs (CBRR), between participating Control Areas (CAs), to reduce the costs of balancing energy. The main objective of INP is to interchange the RRs between participating CAs with opposite signs of interchange power variation. In comparison, the main objective of CBRR is to activate the RRs in participating CAs with the same signs of interchange power variation. Both the INP and CBRR aim to release the RRs and reduce balancing energy as part of the power system's safe operation. The Thesis's main objective is to analyze the impact of the mechanisms for cross-border interchange and activation of the RRs on mutual oscillations of participating CAs and stability for small disturbances. The Thesis's secondary objective is to analyze the impact of the INP and CBRR on frequency quality, on the provision of Load-Frequency Control (LFC), on balancing energy and unintended exchange of energies between participating CAs. Frequency quality in Continental Europe (CE) has been declining in recent years, so it is important that the mechanisms for cross-border interchange and activation of the RRs do not further impair its quality. Both the classic INP and CBRR include a frequency-dependent contribution and, therefore, inherently affect the frequency response of the participating CAs, which is not discussed in the literature. Thus, the impact of the classic INP and CBRR on frequency quality and the provision of LFC is thoroughly evaluated with dynamic simulations of a three-CA test system and eigenvalue analysis of a two-CA system. It is demonstrated that both the classic INP and CBRR reduce the damping of the entire power system. Therefore, a modified implementation of the classic INP and CBRR is presented, and improved INP and CBRR are proposed, which have no impact on the mutual oscillations of participating CAs and stability for small disturbances. Furthermore, the dynamic simulations results confirm that the frequency quality can be improved by the classic INP and CBRR, although there are also cases where it can deteriorate. However, the improved INP and CBRR generally improve the frequency quality in all cases. The improved INP and CBRR also enhance the provision of LFC compared to the classic INP and CBRR. Moreover, the improved INP and CBRR reduce the unintended exchange of energies, thus increasing the economic effects of the INP's and CBRR's activation. The improved INP increases energy exchange, therefore positive economic benefits can be expected in comparison to the system with the classic INP. However, the improved CBRR reduces energy exchange, therefore positive economic benefits can be expected in comparison to the system with the classic CBRR, since energy exchange is paid by CA via bidding process.
Ključne besede: load-frequency control, imbalance netting, cross-border activation, balancing energy, regulating reserves, eigenvalue analysis, performance indicators, area control error, rate of change of frequency, control area
Objavljeno v DKUM: 09.03.2023; Ogledov: 594; Prenosov: 120
Celotno besedilo (14,64 MB)