Wind energy, which generates zero emissions, is an environmentally friendly alternative to conventional electricity generation. For this reason, wind energy is a very popular topic, and there are many studies on this subject. Previous studies have often focused on onshore or offshore installations, lacking comprehensive comparisons and often not accounting for technological advancements and their impact on cost and efficiency. This study addresses these gaps by comparing onshore and offshore wind turbines worldwide in terms of installed capacity, levelized cost of electricity (LCOE), total installed cost (TIC), capacity factor (CF), turbine capacity, hub height, and rotor diameter. Results show that onshore wind power capacity constituted 98.49% in 2010, 97.23% in 2015, and 92.9% in 2022 of the world’s total cumulative installed wind power capacity. Offshore wind capacity has increased yearly due to advantages like stronger, more stable winds and easier installation of large turbine components. LCOE for onshore wind farms decreased from 0.1021 USD/kWh in 2010 to 0.0331 USD/kWh in 2021, while offshore LCOE decreased from 0.1879 USD/kWh in 2010 to 0.0752 USD/kWh in 2021. By 2050, wind energy will contribute to 35% of the global electricity production. This study overcomes previous limitations by providing a comprehensive and updated comparison that incorporates recent technological advancements and market trends to better inform future energy policies and investments.

Solar power production (SPP) using photovoltaics is one of the most effective ways of solar energy utilization. Prediction of SPP is of great importance due to mitigating the effect of random fluctuations in the incoming solar energy and enabling the operator to access solar power output data in advance. Accurate prediction for SPP is also important for providing high-quality electricity to end-consumers. In the present study, a deep learning approach established on Long Short-Term Memory (LSTM) neural network was introduced. The network aimed to forecast one hour-ahead electrical energy production from the solar-PV power plant with 1.15 MW capacity. In addition to the LSTM neural network, two different data-driven methods, namely, adaptive neuro-fuzzy inference system (ANFIS) accompanied by fuzzy c-means (FCM) and ANFIS with grid partition (GP) were applied. The data obtained from the models were also validated using measured data. The results from the comparison revealed that the LSTM model gives the best results with RMSE, MAE, and R equal to 60.66 kWh, 30.47 kWh, and 0.9777, respectively.

Renewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country’s energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.