Purpose

The purpose of this paper is to develop a hybrid algorithm, which is a blend of auto-regressive integral moving average (ARIMA) and multilayer perceptron (MLP) for addressing the non-linearity of the load time series.

Design/methodology/approach

Short-term load forecasting is a complex process as the nature of the load-time series data is highly nonlinear. So, only ARIMA-based load forecasting will not provide accurate results. Hence, ARIMA is combined with MLP, a deep learning approach that models the resultant data from ARIMA and processes them further for Modelling the non-linearity.

Findings

The proposed hybrid approach detects the residuals of the ARIMA, a linear statistical technique and models these residuals with MLP neural network. As the non-linearity of the load time series is approximated in this error modeling process, the proposed approach produces accurate forecasting results of the hourly loads.

Originality/value

The effectiveness of the proposed approach is tested in the laboratory with the real load data of a metropolitan city from South India. The performance of the proposed hybrid approach is compared with the conventional methods based on the metrics such as mean absolute percentage error and root mean square error. The comparative results show that the proposed prediction strategy outperforms the other hybrid methods in terms of accuracy.

中文翻译：

---

电力系统网络短期负荷预测的简化混合算法建模

目的

本文的目的是开发一种混合算法，它是自回归积分移动平均 (ARIMA) 和多层感知器 (MLP) 的混合，用于解决加载时间序列的非线性问题。

设计/方法/方法

短期负荷预测是一个复杂的过程，因为负荷时间序列数据的性质是高度非线性的。因此，仅基于 ARIMA 的负载预测无法提供准确的结果。因此，ARIMA 与 MLP 相结合，这是一种深度学习方法，可以对来自 ARIMA 的结果数据进行建模，并进一步处理它们以对非线性进行建模。

发现

所提出的混合方法检测 ARIMA 的残差，这是一种线性统计技术，并使用 MLP 神经网络对这些残差进行建模。由于在该误差建模过程中近似了负荷时间序列的非线性，所提出的方法产生了每小时负荷的准确预测结果。

原创性/价值

所提出方法的有效性在实验室中使用来自印度南部的一个大城市的真实负载数据进行了测试。基于平均绝对百分比误差和均方根误差等指标，将所提出的混合方法的性能与传统方法进行比较。比较结果表明，所提出的预测策略在准确性方面优于其他混合方法。