Short-Term Load Forecasting: The Similar Shape Functional Time Series Predictor

TL;DR

This paper presents a new functional time series method for short-term load forecasting that uses similar past load shapes to predict future load, demonstrating its effectiveness on real data from Cyprus.

Contribution

The paper introduces a novel similar shape predictor for load forecasting, with proven weak consistency and application to real-world data.

Findings

The method achieves accurate load forecasts on Cyprus data.

It outperforms a recent alternative functional time series approach.

The predictor is theoretically justified with weak consistency.

Abstract

We introduce a novel functional time series methodology for short-term load forecasting. The prediction is performed by means of a weighted average of past daily load segments, the shape of which is similar to the expected shape of the load segment to be predicted. The past load segments are identified from the available history of the observed load segments by means of their closeness to a so-called reference load segment, the later being selected in a manner that captures the expected qualitative and quantitative characteristics of the load segment to be predicted. Weak consistency of the suggested functional similar shape predictor is established. As an illustration, we apply the suggested functional time series forecasting methodology to historical daily load data in Cyprus and compare its performance to that of a recently proposed alternative functional time series methodology for short-term load forecasting.