Kuziboev, Bekhzod; Radulescu, Magdalena; Çatık, A. Nazif; Makhmudov, Samariddin; Balli, Esra; Allayarov, Sardor

Currently economic turbulence and geopolitical fluctuations are causing high prices and supply disruptions in energy markets. As a result, energy consumption risks are becoming more severe. Diversifying energy sources is one way to address these challenges. This study examines how energy source diversification influences energy consumption risk in 36 developed countries from 2000 to 2018, using Method of Moments Quantile Regression (MMQR) and Panel Threshold Regression (PTR) Models. The MMQR captures heterogeneous effects across various levels of energy consumption risk, offering insights into how diversification strategies differ in effectiveness across the risk distribution. The PTR Model identifies potential nonlinear relationships and regime-dependent effects based on economic development levels, enabling an analysis of how the diversification-risk relationship changes across different stages of economic advancement. These methods are well-suited for the non-normal distribution of variables and the potential structural breaks in the relationship between energy diversification and consumption risk. The findings align with theory, revealing that the effects of energy source diversification on energy consumption risk are negative across all quantiles, from 10 % to 90 %. Furthermore, PTR results reveal that energy source diversification reduces energy consumption risk in both the lower and upper regimes of economic development. The study's results provide valuable guidelines for policymakers.

Abdallah, Mohammed; Mohammadi, Babak; Nasiri, Hamid; Katipoğlu, Okan Mert; Abdalla, Modawy Adam Ali; Ebadzadeh, Mohammad Mehdi

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).