Load Forecasting on A Highly Sparse Electrical Load Dataset Using Gaussian Interpolation

TL;DR

This paper demonstrates that Gaussian interpolation effectively handles sparsity in highly sparse load datasets, enabling accurate load forecasting with machine learning and deep learning models, especially LSTM, which outperforms other models.

Contribution

It introduces Gaussian interpolation as a novel approach to address data sparsity in load forecasting and empirically evaluates its effectiveness across multiple models.

Findings

Gaussian interpolation improves data utilization in sparse datasets.

LSTM models outperform other classical and neural network models.

The dataset's sparsity can be mitigated effectively with the proposed method.

Abstract

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.