Open Access

ARTICLE

Photovoltaic Output Prediction and Trading Strategy Based on Fractal Theory

Yifeng Wang^1,*, Wei Cui¹, Zhihui Wang², Yanning Xue², Bing Wang²

1 State Grid Hebei Electric Power Co., Ltd., Shijiazhuang, 050023, China
2 State Grid Hebei Electric Power Co., Ltd., Hengshui Power Supply Branch, Hengshui, 050022, China

* Corresponding Author: Yifeng Wang. Email:

(This article belongs to the Special Issue: Innovative Renewable Energy Systems for Carbon Neutrality: From Buildings to Large-Scale Integration)

Energy Engineering 2026, 123(10), 15 https://doi.org/10.32604/ee.2025.074707

Received 16 October 2025; Accepted 26 November 2025; Issue published 30 August 2026

Abstract

This paper proposes a photovoltaic (PV) output prediction and trading strategy based on fractal theory. Firstly, rescaled range analysis (R/S analysis) is employed to quantify the fractal characteristics of PV output sequences under different weather conditions. By calculating the Hurst exponent and fractal dimension, the self-similarity patterns and complexity differences are revealed. Secondly, a fractal interpolation prediction method based on the iterated function system is constructed to achieve high-precision fitting of typical daily output curves. Finally, by integrating the fractal prediction results, a trading decision-making model aimed at minimizing daily electricity procurement costs is established, and a dynamic pricing mechanism linked to the spot market price is designed to coordinate and optimize the PV consumption ratio and spot market procurement strategy. Case study analysis demonstrates that the proposed strategy can effectively improve the PV consumption rate and reduce the procurement costs for electricity retailers, providing reliable technical support for distributed PV to participate in electricity market trading.

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Keywords

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