Renewable Energy Forecasting in Turkey: Analytical Approaches

An investigation on renewable energy education at the university level in Turkey

Turkey has a large economy and, thus, it expects a very large growth in energy demand. Today, Turkey’s economy is mainly dependent on oil, natural gas, and electricity. On the other hand, Turkey’s energy production meets nearly 35% of its total primary energy consumption. Turkey is an energy importing country. Turkey presently has considerable renewable energy sources. The most important renewable sources are hydropower, wind, solar, geothermal, and biomass. Turkey has about 1% of the total world hydroelectric potential and its significant potential for geothermal power production is ranked seventh in the world. This paper presents the energy situation and potential of the renewable energy sources in Turkey.

Turkey is an energy importing country, more than half of the energy requirement has been supplied by imports. Domestic oil and lignite reserves are limited and lignites are characterised by high ash, sulfur and moisture content. Due to increasing energy consumption, air pollution is becoming a great environmental concern for future in the country. In this regard, renewable energy resources appear to be the one of the most efficient and effective solutions for sustainable energy development and environmental pollution prevention in Turkey. Turkey's geographical location has several advantages for extensive use of most of these renewable energy sources. Because of this and the fact that it has limited fossil fuel resources, a gradual shift from fossil fuels to renewables seems to be serious and the sole alternative for Turkey. This article presents a review of the present energy situation and sustainability, technical and economical potential of renewable energy sources and future policies for energy sector in Turkey. Throughout the paper several problems relating to energy sources, environment and sustainable development are discussed from both current and future perspectives.

Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy consumption prediction (MDLFM-ECP) of RE and NRE power sources. Actual data may influence the prediction performance of the results in prediction approaches. The proposed MDLFM-ECP technique involves pre-processing, fusion-based prediction, and hyperparameter optimization. In addition, the MDLFM-ECP technique involves the fusion of four deep learning (DL) models, namely long short-term memory (LSTM), bidirectional LSTM (Bi-LSTM), deep belief network (DBN), and gated recurrent unit (GRU). Moreover, the chaotic cat swarm optimization (CCSO) algorithm is applied to tune the hyperparameters of the DL models. The design of the CCSO algorithm for optimal hyperparameter tuning of the DL models, showing the novelty of the work. A series of simulations took place to validate the superior performance of the proposed method, and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

Energy production, consumption, policies and recent developments in Turkey

This study focuses on optimizing wind generation plant scheduling to enhance renewable energy utilization while minimizing costs and ensuring grid stability. An advanced forecasting approach is proposed to predict wind generation plant availability for future intervals, integrating accurate demand prediction with renewable energy forecasting. Evaluation metrics like Mean Error (ME), Root Mean Square Error (RMSE), and Correlation assess the proposed solution’s performance, showing its superiority over baseline models with an RMSE of 9.39. Results highlight the effectiveness of the approach in forecasting wind generation plant availability, essential for efficient renewable energy utilization. By reducing reliance on traditional energy sources, the method contributes to cost minimization and promotes sustainability. The study underscores the potential of advanced forecasting techniques in optimizing renewable energy scheduling, offering opportunities for grid stability and reduced environmental impact. The proposed method involves training an LSTM model on preprocessed wind turbine data, integrating it with a scheduling algorithm for optimized task scheduling, and continuously learning to improve efficiency. The Optimized LSTM model outperforms other models in wind power forecasting, providing accurate predictions crucial for energy management decisions. Integration with SCADA control and maintenance scheduling further enhances operational efficiency and cost reduction in wind turbine management.

Current status and future success of renewable energy in Pakistan

Wastewater flow forecasting is key for proper management of wastewater treatment plants (WWTPs). However, to predict the amount of incoming wastewater in WWTPs, wastewater engineers face challenges arising from numerous complexities and uncertainties, such as the nonlinear precipitation-runoff relationships in combined sewer systems, unpredictability due to aging infrastructure, and frequently inconsistent data quality. To address such challenges, a time series analysis model (i.e., the autoregressive integrated moving average, ARIMA) and an artificial neural network model (i.e., the multilayer perceptron neural network, MLPNN) were developed for predicting wastewater inflow. A case study of the Barrie Wastewater Treatment Facility in Barrie, Canada, was carried out to demonstrate the performance of the proposed models. Fifteen-minute flow data over a period of 1 year were collected, and the resampled daily flow data were used to train and validate the developed models. The model performances were examined using root mean square error, mean absolute percentage error, coefficient of determination, and Nash-Sutcliffe efficiency. The results indicate that both models provided reliable forecasts, while ARIMA showed a slightly better performance than MLPNN in this case study. The proposed models can provide useful decision support for the optimization and management of WWTPs.

The transition to a low carbon society is dependent on renewable energy-based electrification. Nevertheless, energy programs have resulted in growing societal polarization in several regions. Therefore, around the globe, government and legislative authorities at the local, regional, national, and international levels are highly concerned about the environmental impacts and risk factors that influence the energy paradigm. Thus, to minimize and/or limit the environmental insecurity issues, the world needs swift and effective actions to secure the climate for a better tomorrow. Moreover, there is a dire need to look for new energy alternatives, along with reducing the complete dependence on petro-based energy sources. Keeping this burning issue in mind, herein, an effort has been made to present the potential of several renewable energy sources, including wind-based renewable energy, solar-based renewable energy, hydro-based renewable energy, and biomass-based renewable energy. Following a brief introduction of energy-related problems and opportunities, a comparative overview is given to renewable vs. non-renewable energy sources. Then, several renewable energy sources, including wind, solar, hydro, and biomass, along with the worldwide energy capacity of each energy source are given with suitable examples and statistics. Finally, risk factors, concluding remarks, and future guidelines are discussed towards the end.

Improving the accuracy of dissolved oxygen (DO) prediction and establishing a water body DO prediction model are of great importance in water environment pollution management and planning management. In this paper, we propose a hybrid model (EEMD-Pearson-LSTM) of ensemble empirical modal decomposition-Pearson analysis and long-short memory neural network (LSTM), which firstly uses EEMD to decompose the non-stationary dissolved oxygen data into several sub-series that are easy to analyze, and secondly uses Pearson correlation analysis method to The screened subsequences are input to the LSTM network for training and prediction. By establishing the conventional LSTM model, EEMD-LSTM model, EEMD-BP model, and EEMD-Pearson-BP model for comparison under different time periods, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R2) were used as evaluation indicators. In predicting the first 90 days of data, the RMSE, MAE, MAPE, and R2 of the EEMD-Pearson-LSTM model were 0.2355, 0.1893, 2.4710, and 0.8787, respectively, which were optimized by 37.88%, 35.44%, 37.42%, and 28.15%, respectively, compared with the traditional LSTM model, and the EEMD- LSTM model by 13.74%, 16.46%, 16.82%, and 4.98%, respectively, and the error of EEMD-BP network by 23.93%, 22.70%, and 24.32%, respectively, and its R2 by 11.17%, and the error of EEMD-Pearson-BP network by 18.62%, 14.07%, and 14.44%, and its R2 improved by 7.58%. To further demonstrate the advantages of EEMD-Pearson-LSTM, the prediction models for 30-day and 60-day time periods were selected for comparison, and the results showed that EEMD-Pearson-LSTM outperformed other models for the prediction of dissolved oxygen content in different time periods.

Indispensable role of coal as an energy source in Turkey with focus on biodesulphurization studies and advances

Accurately forecasting financial markets remains a central challenge in economics due to the volatile, nonlinear, and complex nature of asset price movements. Traditional statistical models like Autoregressive Generalized Autoregressive Conditional Heteroskedasticity (AR-GARCH) have been widely used for modeling volatility, but often fall short when addressing intricate market patterns. This study systematically compares the predictive capabilities of AR-GARCH, Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), Random Forest (RF), and a hybrid ensemble on weekly data from the S&P 500, FTSE 100, and Nikkei 225 indices spanning 2000 to 2024. Performance is evaluated using standard forecasting metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The findings indicate that while each separate model has varying strengths depending on market conditions, the hybrid model consistently achieves superior accuracy by leveraging the diversity of all approaches. These results highlight the benefits of integrating classical econometric approaches with contemporary machine learning techniques to improve forecasting precision and strengthen the robustness of models in financial time series analysis.

This paper is a selective review of the development of the threshold model in time series analysis over the past 30 years or so. First, it re-visits the motivation of the model. Next, it describes the various expressions of the model, highlighting the principle underlying them and the main probabilistic and statistical properties. Finally, after listing some of the recent offsprings of the threshold model, it finishes with some on-going research in the context of threshold volatility.

BackgroundGonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.MethodsTime series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.ResultsThe Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE.ConclusionsThe overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

ABSTRACTThe growing demand for energy in developing societies, coupled with dwindling conventional resources, necessitates the adoption of renewable energy (RE) sources. A comprehensive understanding of these sources and their optimal utilization is crucial. The awareness of the users plays a very important role in the effective utilization of different energy sources. Hence, this study examines the level of RE awareness in Palestine, identifying factors influencing public perception and exploring potential strategies for promoting RE adoption. This awareness depends on different factors related to the users' education level, community type, and type of building. Different factors that affect the willingness and attitude to gain knowledge about RE are presented. In addition, various practices highlighting the increasing level of awareness regarding the adoption of RE sources are investigated. In addition, the status of RE education and its potential impact on engineers have also been discussed. Various individual and institutional efforts are highlighted in the study as strategies to bridge the gap and overcome obstacles to adopting RE sources. Suggestions for better use of these resources have been highlighted. Thereafter, recommendations for effective RE implementation in Palestine are analyzed and conclusions are drawn. The main challenges in integrating RE into Palestine's existing energy grid and system include infrastructure limitations, resources and financial constraints, and political issues. Still, there are possibilities in the form of international collaboration and investment. A flexible and modular RE grid could also address some of these challenges by integrating small‐scale systems that can be scaled up as needed.