用高斯插值处理62%稀疏的电力负荷数据，实现精准预测。
Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation
- 采用高斯插值填补62%缺失的小时级负荷数据
- 基于插值数据训练模型，LSTM表现最优
- 适合处理实际中大量缺失的电力负荷预测任务
数据稀疏性（即数据中存在缺失或零值）在真实场景中常带来挑战。本文研究了一组约62%稀疏的电厂小时级负荷数据，在假设其为广义平稳（WSS）的前提下，通过高斯插值方法进行数据增强。我们对数据进行了统计分析，并训练了多种机器学习与深度学习模型。实验表明，高斯插值能有效应对稀疏性问题，且在多种模型中，基于LSTM的神经网络表现最佳。结果验证了该方法在稀疏负荷预测中的适用性。
原文摘要 · Abstract (English)
Sparsity, defined as the presence of missing or zero values in a dataset, often poses a major challenge while operating on real-life datasets. Sparsity in features or target data of the training dataset can be handled using various interpolation methods, such as linear or polynomial interpolation, spline, moving average, or can be simply imputed. Interpolation methods usually perform well with Strict Sense Stationary (SSS) data. In this study, we show that an approximately 62\% sparse dataset with hourly load data of a power plant can be utilized for load forecasting assuming the data is Wide Sense Stationary (WSS), if augmented with Gaussian interpolation. More specifically, we perform statistical analysis on the data, and train multiple machine learning and deep learning models on the dataset. By comparing the performance of these models, we empirically demonstrate that Gaussian interpolation is a suitable option for dealing with load forecasting problems. Additionally, we demonstrate that Long Short-term Memory (LSTM)-based neural network model offers the best performance among a diverse set of classical and neural network-based models.
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