ABSTRACT

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.

摘要

可再生能源在世界范围内发展迅速，因为它们是无限的和永久的，可在每个国家使用，并且消除了对外国的依赖。在这方面，准确的可再生能源发电量（REG）预测对于一个国家与其发展有关的能源规划至关重要。在这项研究中，应用了两种不同的数据驱动方法，例如具有模糊c均值（FCM）和长短期记忆（LSTM）神经网络的自适应神经模糊推理系统（ANFIS），来进行为期一天的短时提前长期REG预测。此外，还使用这些方法进行了短期水力发电（HEG），地热发电（GEG）和生物能源发电（BEG）的预测。相关系数（R），均方根误差（RMSE），平均绝对误差（MAE），使用平均绝对百分比误差（MAPE）作为评估标准。通过评估它们的误差，将ANFIS-FCM和LSTM模型预测的值与实际值进行比较。根据MAPE评估标准获得的测试结果，获得了GEG的最佳估计模型。对于REG，HEG，GEG和BEG估计值，最低MAPE值分别为7.20％，7.46％，1.63％和2.46％。结果表明，ANFIS和LSTM模型在每日REG预测中均表现出令人满意的性能，而ANFIS和LSTM模型给出的结果几乎相同。根据MAPE评估标准获得的测试结果，获得了GEG的最佳估计模型。对于REG，HEG，GEG和BEG估计，最低的MAPE值分别为7.20％，7.46％，1.63％和2.46％。结果表明，ANFIS和LSTM模型在每日REG预测中均表现出令人满意的性能，而ANFIS和LSTM模型给出的结果几乎相同。根据MAPE评估标准获得的测试结果，获得了GEG的最佳估计模型。REG，HEG，GEG和BEG估计值的最低MAPE值分别为7.20％，7.46％，1.63％和2.46％。结果表明，ANFIS和LSTM模型在每日REG预测中均表现出令人满意的性能，而ANFIS和LSTM模型给出的结果几乎相同。