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Related Experiment Video

Updated: Jul 5, 2025

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Application of AI for Short-Term PV Generation Forecast.

Helder R O Rocha¹, Rodrigo Fiorotti^1,2, Jussara F Fardin¹

¹Department of Electrical Engineering, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Vitória 29075-910, ES, Brazil.

Sensors (Basel, Switzerland)

|January 11, 2024

Summary

Accurate photovoltaic power forecasting is crucial. Temporal Convolutional Network (TCN) significantly outperforms Long Short-Term Memory (LSTM) and Bidirectional LSTM for predicting PV power, voltage, and efficiency over 15-minute and 24-hour horizons.

Keywords:

BILSTM LSTM TCN artificial intelligence photovoltaic power short-term forecast

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Area of Science:

Renewable Energy Systems
Artificial Intelligence in Energy
Photovoltaic Power Generation

Background:

Efficient utilization of solar energy necessitates precise photovoltaic (PV) power generation forecasting.
Accurate estimations are vital for grid integration and energy management.

Purpose of the Study:

To evaluate and compare the forecasting performance of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Temporal Convolutional Network (TCN) for PV power, voltage, and efficiency.
To identify the most effective deep learning technique for short-term PV generation prediction.

Main Methods:

Deep learning models including LSTM, Bidirectional LSTM, and TCN were applied to forecast PV parameters.
Experimental data from a 1320 Wp amorphous PV plant in Madrid, Spain, collected over one year, was used for training and validation.
Forecasts were made for 15-minute and 24-hour horizons.

Main Results:

Temporal Convolutional Network (TCN) demonstrated superior performance across all forecast variables and horizons compared to LSTM and Bidirectional LSTM.
TCN achieved a Mean Squared Error (MSE) of 0.0024 for 15-minute forecasts and 0.0058 for 24-hour forecasts.
Sensitivity analysis indicated that TCN accuracy decreases with longer forecast horizons, yet 6 months of data proved sufficient for adequate results.

Conclusions:

TCN is a highly effective deep learning model for accurate photovoltaic power, voltage, and efficiency forecasting.
The findings support the adoption of TCN for improving the operational efficiency of solar power systems.
Even with extended forecast horizons, TCN provides reliable predictions with sufficient training data.