当前位置: X-MOL 学术IEEE Access › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach
IEEE Access ( IF 3.9 ) Pub Date : 2021-02-18 , DOI: 10.1109/access.2021.3060290
Behnam Farsi , Manar Amayri , Nizar Bouguila , Ursula Eicker

Since electricity plays a crucial role in countries’ industrial infrastructures, power companies are trying to monitor and control infrastructures to improve energy management and scheduling. Accurate forecasting is a critical task for a stable and efficient energy supply, where load and supply are matched. This article discusses various algorithms and a new hybrid deep learning model which combines long short-term memory networks (LSTM) and convolutional neural network (CNN) model to analyze their performance for short-term load forecasting. The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as well as “daily power electric consumption of Germany”, are used to test and compare the presented models. To evaluate the tested models’ performance, root mean squared error (RMSE), mean absolute percentage error (MAPE), and R-squared were used. In total, this article is divided into two parts. In the first part, different machine learning models, including the PLCNet, predict the next time step load. In the second part, the model’s performance, which has shown the most accurate results in the first part, is discussed in different time horizons. The results show that deep neural networks models, especially PLCNet, are good candidates for being used as short-term prediction tools. PLCNet improved the accuracy from 83.17% to 91.18% for the German data and achieved 98.23% accuracy in Malaysian data, which is an excellent result in load forecasting.

中文翻译:

基于机器学习技术和新型并行深度LSTM-CNN方法的短期负荷预测

由于电力在国家的工业基础设施中起着至关重要的作用,因此电力公司正在尝试监视和控制基础设施以改善能源管理和调度。准确的预测是稳定,高效的能源供应(负载和供应相匹配)的关键任务。本文讨论了各种算法和一种新的混合深度学习模型,该模型结合了长短期记忆网络(LSTM)和卷积神经网络(CNN)模型来分析其性能,以进行短期负荷预测。提出的模型称为并行LSTM-CNN网络或PLCNet。使用两个现实世界的数据集,即“马来西亚的小时负载消耗量”和“德国的每日电力消耗量”,来测试和比较所介绍的模型。为了评估测试模型的性能,使用均方根误差(RMSE),均值绝对百分比误差(MAPE)和R平方。总而言之,本文分为两个部分。在第一部分中,包括PLCNet在内的不同机器学习模型将预测下一步负载。在第二部分中,将在不同的时间范围内讨论该模型的性能,该模型的性能在第一部分中显示了最准确的结果。结果表明,深度神经网络模型,尤其是PLCNet,可以用作短期预测工具。PLCNet将德国数据的准确性从83.17%提高到91.18%,在马来西亚数据中达到了98.23%的准确性,这在负荷预测中是一个极好的结果。包括PLCNet在内的不同机器学习模型可以预测下一步负载。在第二部分中,将在不同的时间范围内讨论该模型的性能,该模型的性能在第一部分中显示了最准确的结果。结果表明,深度神经网络模型,尤其是PLCNet,可以用作短期预测工具。PLCNet将德国数据的准确性从83.17%提高到91.18%,在马来西亚数据中达到了98.23%的准确性,这在负荷预测中是一个极好的结果。包括PLCNet在内的不同机器学习模型可以预测下一步负载。在第二部分中,将在不同的时间范围内讨论该模型的性能,该模型的性能在第一部分中显示了最准确的结果。结果表明,深度神经网络模型,尤其是PLCNet,可以用作短期预测工具。PLCNet将德国数据的准确性从83.17%提高到91.18%,在马来西亚数据中达到了98.23%的准确性,这在负荷预测中是一个极好的结果。是用作短期预测工具的良好候选者。PLCNet将德国数据的准确性从83.17%提高到91.18%,在马来西亚数据中达到了98.23%的准确性,这在负荷预测中是一个极好的结果。是用作短期预测工具的良好候选者。PLCNet将德国数据的准确性从83.17%提高到91.18%,在马来西亚数据中达到了98.23%的准确性,这在负荷预测中是一个极好的结果。
更新日期:2021-03-02
down
wechat
bug