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Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture

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  • Saravanakumar Venkatesan

    (Department of Information and Communications Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea)

  • Yongyun Cho

    (Department of Information and Communications Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea)

Abstract

Since the advent of smart agriculture, technological advancements in solar energy have significantly improved farming practices, resulting in a substantial revival of different crop yields. However, the smart agriculture industry is currently facing challenges posed by climate change. This involves multi-timeframe forecasts for greenhouse operators covering short-, medium-, and long-term intervals. Solar energy not only reduces our reliance on non-renewable electricity but also plays a pivotal role in addressing climate change by lowering carbon emissions. This study aims to find a method to support consistently optimal solar energy use regardless of changes in greenhouse conditions by predicting solar energy (kWh) usage on various time steps. In this paper, we conducted solar energy usage prediction experiments on time steps using traditional Tensorflow Keras models (TF Keras), including a linear model (LM), Convolutional Neural Network (CNN), stacked—Long Short Term Memory (LSTM), stacked-Gated recurrent unit (GRU), and stacked-Bidirectional—Long Short —Term Memory (Bi-LSTM), as well as Tensor-Flow-based models for solar energy usage data from a smart farm. The stacked-Bi-LSTM outperformed the other DL models with Root Mean Squared Error (RMSE) of 0.0048, a Mean Absolute Error (MAE) of 0.0431, and R-Squared ( R 2 ) of 0.9243 in short-term prediction (2-h intervals). For mid-term (2-day) and long-term (2-week) forecasting, the stacked Bi-LSTM model also exhibited superior performance compared to other deep learning models, with RMSE values of 0.0257 and 0.0382, MAE values of 0.1103 and 0.1490, and R 2 values of 0.5980 and 0.3974, respectively. The integration of multi-timeframe forecasting is expected to avoid conventional solar energy use forecasting, reduce the complexity of greenhouse energy management, and increase energy use efficiency compared to single-timeframe forecasting models.

Suggested Citation

  • Saravanakumar Venkatesan & Yongyun Cho, 2024. "Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture," Energies, MDPI, vol. 17(17), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:17:p:4322-:d:1466745
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    References listed on IDEAS

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    3. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
    4. Azizi, Narjes & Yaghoubirad, Maryam & Farajollahi, Meisam & Ahmadi, Abolfzl, 2023. "Deep learning based long-term global solar irradiance and temperature forecasting using time series with multi-step multivariate output," Renewable Energy, Elsevier, vol. 206(C), pages 135-147.
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