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Volume 150
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10.3390/engproc2026150090
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Proceeding Paper

Data-Driven Electricity Forecasting for Small Grid-Tied Photovoltaic Power Plants in the Transition from Centralized to Distributed Generation †

by
Rumen Mihailov
* and
Vladimir Valkanov
Department of Computer Systems, Faculty of Mathematics and Informatics, Paisii Hilendarski University of Plovdiv, Plovdiv 4000, Bulgaria
*
Author to whom correspondence should be addressed.
†
Presented at the 15th International Scientific Conference TechSys 2026—Engineering, Technologies and Systems, Plovdiv, Bulgaria, 14–16 May 2026.
Eng. Proc. 2026, 150(1), 90; https://doi.org/10.3390/engproc2026150090
Published: 30 July 2026
(This article belongs to the Proceedings of The 15th International Scientific Conference TechSys 2026—Engineering, Technologies and Systems)
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Abstract

The rapid expansion of renewable weather-dependent electricity generators has created a challenge in the way grid operators manage electricity dispatch. This article explores the core challenges of managing the instantaneous balance between generation and consumption needed to maintain healthy grid operation. The magnitude of the challenge is depicted clearly by the 2025 Iberian Peninsula blackout, listing as a main contributing factor the large penetration of renewable energy in the grid. The article proposes a model to better forecast photovoltaic production and limit grid entropy. It also outlines how the implementation of such a model would lead to increased feed-in prices for producers and offset renewable cannibalization as well as a lower end-user electricity bill.
Keywords: renewable energy; Internet of Things (IoT); data processing; electrical grid system; forecasting; monitoring; photovoltaics renewable energy; Internet of Things (IoT); data processing; electrical grid system; forecasting; monitoring; photovoltaics

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MDPI and ACS Style

Mihailov, R.; Valkanov, V. Data-Driven Electricity Forecasting for Small Grid-Tied Photovoltaic Power Plants in the Transition from Centralized to Distributed Generation. Eng. Proc. 2026, 150, 90. https://doi.org/10.3390/engproc2026150090

AMA Style

Mihailov R, Valkanov V. Data-Driven Electricity Forecasting for Small Grid-Tied Photovoltaic Power Plants in the Transition from Centralized to Distributed Generation. Engineering Proceedings. 2026; 150(1):90. https://doi.org/10.3390/engproc2026150090

Chicago/Turabian Style

Mihailov, Rumen, and Vladimir Valkanov. 2026. "Data-Driven Electricity Forecasting for Small Grid-Tied Photovoltaic Power Plants in the Transition from Centralized to Distributed Generation" Engineering Proceedings 150, no. 1: 90. https://doi.org/10.3390/engproc2026150090

APA Style

Mihailov, R., & Valkanov, V. (2026). Data-Driven Electricity Forecasting for Small Grid-Tied Photovoltaic Power Plants in the Transition from Centralized to Distributed Generation. Engineering Proceedings, 150(1), 90. https://doi.org/10.3390/engproc2026150090

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