Design and Development of a Weather Prediction Device for Solar Power Plant Projects Using a Backpropagation Artificial Neural Network
Abstract
Planning the installation of a Solar Power Plant (PLTS) requires weather condition information so that projects can be implemented according to plan. However, monitoring weather conditions and forecasting weather for several days to one month ahead have not been optimally utilized as a basis for decision-making in PLTS project planning. Therefore, this study aims to design and develop a weather prediction device based on the Backpropagation Artificial Neural Network (ANN) algorithm so that weather conditions for one month ahead can be predicted accurately. The sensors used were a BH1750 sensor to measure solar light intensity, a BME280 sensor to measure temperature, humidity, and air pressure, a wind-speed sensor, and a rainfall sensor. A total of 10,080 sensor data records were obtained and stored on a microSD module. The data were then processed as inputs for the Backpropagation Artificial Neural Network algorithm to generate weather predictions. The results show that the combination of the Backpropagation Artificial Neural Network algorithm successfully produced a one-month weather prediction with the lowest Mean Absolute Error (MAE) of 0.218595.
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