Energy forecasting has a vital role to play in smart grid (SG) systems involving various applications such as demand-side management, load shedding, optimum dispatch, etc. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty and granularity in the SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems considering the different models and architectures. Traditional statistical and machine learning-based forecasting methods are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid methods and data pre-processing techniques for better forecasting accuracy is also highlighted. A comparative case study using the Victorian electricity consumption benchmark and American electric power (AEP) datasets is conducted to analyze the performance of different forecasting methods. The analysis demonstrates higher accuracy of the recurrent neural network (RNN) and long-short term memory (LSTM) methods when sample sizes are larger and hyperparameters are appropriately tuned. Furthermore, hybrid methods such as CNN-LSTM are also highly effective to deal with long sequences in energy data.
翻译:考虑到SG数据的不确定性和颗粒性,对智能电网系统进行能源预测,在涉及需求方管理、卸载、最佳发送等各种应用的智能电网系统中可发挥至关重要的作用。 管理高效预测,同时确保尽可能少的预测错误,是当今电网面临的主要挑战之一,考虑到SG数据的不确定性和颗粒性。本文件对考虑不同模型和结构的SG系统最新预测方法进行全面和面向应用的审查。对传统的基于统计和机器学习的预测方法是否适用于能源预测进行了广泛调查。此外,还强调了混合方法和数据预处理技术对于提高预测准确性的重要性。利用维多利亚州电力消费基准和美国电力(电力)数据集进行比较案例研究,以分析不同预测方法的性能。分析表明,当抽样大小较大、超光谱度时,经常神经网络(NNN)和长期短期记忆(LSTM)方法的准确性更高。此外,CNN-LSTM等混合方法对于处理长序能源数据也非常有效。