考虑数据分布偏移的短期居民净负荷预测方法

doi:10.12204/j.issn.1000-7229.2024.02.008

摘要/Abstract

摘要：

新能源发电的高不确定性导致净负荷的数据分布偏移更加严重。数据分布偏移导致模型在历史数据中学习到的特征信息不再完全适用于未来数据,从而给净负荷预测(net load forecasting,NLF)带来了挑战。因此,考虑到净负荷中更严重的数据分布偏移问题,提出了一种基于不变风险最小化-不确定性加权-长短期记忆神经网络(long short-term memory neural network,LSTM)的短期居民净负荷预测方法,以提升净负荷预测精度。首先,通过不变风险最小化(invariant risk minimization,IRM)建立了一个双目标问题,包括准确预测和学习跨不同数据分布的不变特征。其次,通过长短期记忆神经网络(long short-term memory neural network,LSTM)处理时间序列数据的非线性特征。然后,通过基于不确定性加权(uncertainty weighting,UW)的目标平衡机制避免过度实现任一目标。此外,通过引入分位数回归将所提方法扩展到概率预测。最后,通过基于澳大利亚Ausgrid公司提供的真实居民电表数据从确定性预测结果、概率预测结果、不同数据分布偏移程度和不同光伏渗透率等多个维度验证了所提方法的有效性。

关键词: 短期居民净负荷预测, 数据分布偏移, 不变风险最小化, 长短期记忆神经网络, 不确定性加权

Abstract:

As new energy generation incurs more uncertainty, it leads to a more severe shift in net load data distribution. The data distribution shift means that the feature information learned by the model in historical data is no longer fully applicable to future data, thus posing a significant challenge to net load forecasting (NLF). Therefore, considering the data distribution shift problem in net load, this study proposes a short-term residential net load forecasting method based on IRM-UW-LSTM to improve net load forecasting accuracy. First, a dual-objective problem was established using invariant risk minimization (IRM), which includes accurate forecasting and learning of invariant features across different data distributions. Second, a long short-term memory neural network (LSTM) was used to deal with the nonlinear features of the time series data. Subsequently, an uncertainty weighting (UW)-based objective balancing mechanism was used to avoid overachieving either objective. In addition, a quantile regression method was introduced to extend this study to probabilistic forecasting. Finally, the effectiveness of the proposed method was verified using multiple dimensions of deterministic and probabilistic prediction results, different data distribution shift levels, and different PV penetration rates based on real residential meter data provided by Ausgrid, Australia.

Key words: short-term residential net load forecasting, data distribution shift, invariant risk minimization, long short-term memory neural network, uncertainty weighting

中图分类号:

TM714

王瑞临, 赵健, 孙智卿, 宣羿. 考虑数据分布偏移的短期居民净负荷预测方法[J]. 电力建设, 2024, 45(2): 90-101.

WANG Ruilin, ZHAO Jian, SUN Zhiqing, XUAN Yi. Research on Short-term Residential Net Load Forecasting Method Considering Data Distribution Shift[J]. ELECTRIC POWER CONSTRUCTION, 2024, 45(2): 90-101.

图/表 16

图1 不同年份同一季节数据分布偏移比较

Fig.1 The compare of the degree of the data distribution shift under the same season for different years

图2 同一季节相邻月份数据分布偏移比较

Fig.2 The compare of the degree of data distribution shift adjacent months under the same season

图3 净负荷预测中的不变特征信息示例

Fig.3 Invariant feature information in the net load forecasting

图4 LSTM结构

Fig.4 A unit of LSTM

表1 特征建立

Table 1 Feature establishment

图5 基于IRM-UW-LSTM的净负荷预测整体框架

Fig.5 The process of the net load forecasting method based on IRM-UW-LSTM

表2 超参数值

Table 2 Hyperparameter value

表3 确定性净负荷预测结果

Table 3 Deterministic net load forecasting results

图6 不同方法下确定性净负荷预测结果

Fig.6 Deterministic net load forecasting results under different methods

图7 净负荷概率预测结果

Fig.7 Probabilistic net load forecasting results

表4 不同方法下概率净负荷预测评估结果

Table 4 Probabilistic net load forecast evaluation results under different methods

图8 不同程度数据分布偏移情况下所提方法与次优方法的MAAPE、RMSE结果

Fig.8 MAAPE and RMSE results of proposed method and the second-best method under different degrees of data distribution shift

图9 不同数据分布偏移程度下所提方法与次优方法相比MAAPE、RMSE的提升程度

Fig.9 Relative improvement of proposed method compared to the second-best method under different degrees of data distribution shift

图10 不同光伏渗透率下所提方法与次优方法的MAAPE、RMSE结果

Fig.10 MAAPE and RMSE results of the proposed method and the suboptimal method under different photovoltaic permeability

表5 不同RNN网络的净负荷预测结果

Table 5 Net load forecasting results for different RNN networks

表6 不同RNN网络的计算时间

Table 6 Computational time for different RNN network

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