Forecasting Installation Demand Using Machine Learning: Evidence from a Large PV Installer in Poland

Anna Zielińska () and Rafał Jankowski
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Anna Zielińska: Faculty of Management, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Krakow, Poland
Rafał Jankowski: Faculty of Management, AGH University of Krakow, al. A. Mickiewicza 30, 30-059 Krakow, Poland

Energies, 2025, vol. 18, issue 18, 1-30

Abstract: The dynamic growth of the photovoltaic (PV) market in Poland, driven by declining technology costs, government support programs, and the decentralization of energy generation, has created a strong demand for accurate short-term forecasts to support sales planning, logistics, and resource management. This study investigates the application of long short-term memory (LSTM) recurrent neural networks to forecast two key market indicators: the monthly number of completed PV installations and their average unit capacity. The analysis is based on proprietary two-year data from one of the largest PV companies in Poland, covering both sales and completed installations. The dataset was preprocessed through cleaning, filtering, and aggregation into a consistent monthly time series. Results demonstrate that the LSTM model effectively captured seasonality and temporal dependencies in the PV market, outperforming multilayer perceptron (MLP) models in forecasting installation counts and providing robust predictions for average capacity. These findings confirm the potential of LSTM-based forecasting as a valuable decision-support tool for enterprises and policymakers, enabling improved market strategy, optimized resource allocation, and more effective design of support mechanisms in the renewable energy sector. The originality of this study lies in the use of a unique, proprietary dataset of over 12,000 completed PV micro-installations, rarely available in the literature, and in its direct focus on market demand forecasting rather than energy production. This perspective highlights the practical value of the model for companies in sales planning, logistics, and resource allocation.

Keywords: photovoltaics; machine learning; long short-term memory; LSTM; recurrent neural networks; RNNs; artificial intelligence; energy forecasting; prosumer systems; smart grids; PV investment; renewable energy; data analytics; operational efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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