Non-Parametric Estimation of Multiple Periodic Components in Turkey's Electricity Consumption

TL;DR

This paper introduces a non-parametric method using bandpass filtering and bootstrap techniques to accurately estimate multiple periodic components in Turkey's electricity consumption data, improving understanding of daily, weekly, and annual patterns.

Contribution

The study presents a novel bootstrap-based approach (VBPBB) for estimating multiple periodic components in electricity consumption, outperforming existing methods in accuracy and confidence interval precision.

Findings

Identified daily, weekly, and annual periodic patterns in Turkey's electricity consumption.

VBPBB provides narrower confidence intervals compared to other bootstrap methods.

Method enhances prediction accuracy for electricity demand forecasting.

Abstract

Electric generation and consumption are an essential component of contemporary living, influencing diverse facets of our daily routines, convenience, and economic progress. There is a high demand for characterizing the periodic pattern of electricity consumption. VBPBB employs a bandpass filter aligned to retain the frequency of a PC component and eliminating interference from other components. This leads to a significant reduction in the size of bootstrapped confidence intervals. Furthermore, other PC bootstrap methods preserve one but not multiple periodically correlated components, resulting in superior performance compared to other methods by providing a more precise estimation of the sampling distribution for the desired characteristics. The study of the periodic means of Turkey electricity consumption using VBPBB is presented and compared with outcomes from alternative bootstrapping approaches. These findings offer significant evidence supporting the existence of daily, weekly, and annual PC patterns, along with information on their timing and confidence intervals for their effects. This information is valuable for enhancing predictions and preparations for future responses to electricity consumption.