# Research on time series prediction of microclimate in agrivoltaic systems based on the long short-term memory and attention mechanism

## TL;DR

This paper introduces a new model combining LSTM and attention mechanisms to accurately predict microclimate conditions in agrivoltaic systems.

## Contribution

The novel LSTM-Attention model improves time-series prediction of solar radiation and air temperature in agrivoltaic systems.

## Key findings

- The LSTM-Attention model reduced RMSE for solar radiation predictions by up to 42.1% compared to other models.
- Air temperature prediction errors dropped by 39.0% in summer and 18.1% in winter using the LSTM-Attention model.
- The model maintained stable performance even in winter and rainy weather conditions.

## Abstract

Agrivoltaic (AV) systems combine photovoltaic (PV) power generation with agriculture to enhance land use and energy production. However, accurately predicting the microclimate within AV systems remains a challenge, primarily due to existing models failing to get their inherent temporal and spatial variability.

To address this, this study used long short-term memory (LSTM) networks to process time-series data and incorporated an attention mechanism to adjust the importance of temporal features. The model considered two environmental parameters, including solar radiation intensity and air temperature. Data collected from experimental AV systems with different PV panel density in Nanjing, China. The performance of the LSTM-Attention model was compared with traditional machine learning methods and standard LSTM models.

The results demonstrated that the LSTM-Attention model outperformed the other models in predicting both solar radiation intensity and air temperature within AV systems with different PV panel density. Specifically, the Root Mean Square Error (RMSE) for radiation intensity predictions decreased by 28.0%, 35.7%, and 42.1% at different coverage densities. For air temperature predictions, the RMSE dropped by 39.0% in summer and 18.1% in winter. Importantly, the LSTM-Attention model maintained stable prediction performance even in winter and rainy weather conditions.

The results indicated that the LSTM-Attention model could effectively captured the complex temporal variations in solar radiation and air temperature within AV systems, especially under varying weather conditions. The study provides theoretical support for improving crop management strategies within AV systems.