Gayathry V, Kaliyaperumal Deepa, Salkuti Surender Reddy
Department of EEE, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Bengaluru, India.
Department of Railroad and Electrical Engineering, Woosong University, Daejeon, Republic of Korea.
Sci Rep. 2024 Aug 2;14(1):17945. doi: 10.1038/s41598-024-68531-3.
Renewable integration in utility grid is crucial in the current energy scenario. Optimized utilization of renewable energy can minimize the energy consumption from the grid. This demands accurate forecasting of renewable contribution and planning. Most of the researches aim to find a suitable forecasting model in terms of accuracy and error metrics. However, the uncertainty and variability in these forecasts are also significant. This work combines point forecast with interval forecast to provide comprehensive information about the forecast uncertainty. In this work, solar irradiance forecasting is carried out using artificial intelligence (AI) techniques. Forecasting is done using seasonal auto-regressive moving average with exogenous factors (SARIMAX), support vector regression (SVR), long short term memory (LSTM) techniques and performance is evaluated. SVR model exhibited the best performance with R values of 0.97 and 0.96 for winter and summer respectively and 0.85 for monsoon and post-monsoon seasons. This is followed by forecast error distribution studies and uncertainty analysis. For this, SVR forecast error data is fitted using laplace distribution. Uncertainty study is carried out using confidence intervals and coverage rates. Excellent coverage rates are obtained for various confidence levels for all seasons, indicating the appropriate fitting of error distribution. For the narrow 85% confidence band, coverage rates of 89%, 95%, 90%, and 88% are obtained for winter, summer, monsoon and post-monsoon respectively. The work emphasizes the need for error-distribution studies, modeling of forecast errors and their application in providing reliable forecast intervals with the perspective of enhancing system reliability.
在当前能源形势下，可再生能源并入公用电网至关重要。优化可再生能源利用可将电网能源消耗降至最低。这就需要准确预测可再生能源的贡献并进行规划。大多数研究旨在根据准确性和误差指标找到合适的预测模型。然而，这些预测中的不确定性和变异性也很显著。这项工作将点预测与区间预测相结合，以提供有关预测不确定性的全面信息。在这项工作中，利用人工智能（AI）技术进行太阳辐照度预测。使用带有外生因素的季节性自回归移动平均模型（SARIMAX）、支持向量回归（SVR）、长短期记忆（LSTM）技术进行预测，并对性能进行评估。SVR模型表现出最佳性能，冬季和夏季的R值分别为0.97和0.96，季风和季风后季节为0.85。接下来是预测误差分布研究和不确定性分析。为此，使用拉普拉斯分布拟合SVR预测误差数据。使用置信区间和覆盖率进行不确定性研究。所有季节在不同置信水平下均获得了出色的覆盖率，表明误差分布拟合得当。对于较窄的85%置信带，冬季、夏季、季风和季风后季节的覆盖率分别为89%、95%、90%和88%。这项工作强调了误差分布研究、预测误差建模及其在提供可靠预测区间以提高系统可靠性方面的应用的必要性。
