Performance Estimation Modeling via Machine Learning of an Agrophotovoltaic System in South Korea

Abstract

The Agrophotovoltaic (APV) system is a novel concept in the field of Renewable Energy Systems. This system enables the generation of solar energy via photo-voltaic (PV) modules above crops, to mitigate harmful impact on food production. This study aims to develop a performance evaluation model for an APV system in a temperate climate region, such as South Korea. To this end, both traditional electricity generation models (solar radiation-based model and climate-based model) of PV modules and two major machine learning (ML) techniques (i.e., polynomial regression and deep learning) have been considered. Electricity generation data was collected via remote sensors installed in the APV system at Jeollanam-do Agricultural Research and Extension Services in South Korea. Moreover, economic analysis in terms of cost and benefit of the subject APV system was conducted to provide information about the return on investment to farmers and government agencies. As a result, farmers, agronomists, and agricultural engineers can easily estimate performance and profit of their APV systems via the proposed performance model.

A systematic review on predicting PV system parameters using machine learning

“…It is more prominent in solar experiments since researchers can postulate solar panels' performance and durability using computational tools in either simulation or practical experiments. During our screening, we found that some authors performed their studies practically in real time [35] , [36] , [58] , and many authors adopted simulation tools with curated data sets to obtain their findings [5] , [67] , [4] . In another situation, simulation and practical approaches have been applied in the same experiments for a better comparative analysis [50] , [61] , [41] .…”

“…Table 3 reveals that ANN wins in terms of best performing ML methods [38] , [32] , [50] , [54] , [58] , [61] . Jung et al [54] achieved 97% score in their testing dataset using ANN, whereas Kim and Kim [36] achieved 96.42% and Zhao et al [50] achieved 82.90% score. Along with ANN, other ML approaches, such as BPNN, ANFIS, RVFL, and RF, were extensively applied in the PV performance tests.…”

“…As discussed earlier, several authors conducted their experiments in several geolocations. However, to create a generic predictor, which is not heavily influenced by location-specific data, we will need to update the models with a lot more data from around the world [36] , [43] . Some authors have advised upgrading the essential network parameters and tuning the model and data training process to increase performance [4] .…”

Exploring the operational potential of the forest-photovoltaic utilizing the simulated solar tree

“…The principle of the forest-photovoltaic is that the solar tree utilizes the remaining sunlight used for forest growth. The agrophotovoltaic system is a concept that produces crops and electricity in parallel in farmland and has recently been widely known as the solar sharing theory 12 , 13 . As the word itself means, the forest-photovoltaic system is to apply the concept of the agrophotovoltaic system to forests.…”

Analisis Klasifikasi Kematangan Buah Tomat dengan Pendekatan Transfer Learning Model EfficientNet

“…Pertanian Pintar adalah sistem pertanian yang dapat meningkatkan efisien serta produtivitas pertanian, menerapkan konsep teknologi dan mekanisasi kebidang pertanian [1]- [16]. Pertanian Pintar dapat diterapkan pada lahan sempit dan jumlah pertani yang sedikit ,serta dapat mengkondisikan iklim micro sesuai kondisi tanaman seperti yang diajukan oleh Oktarina et al pada tahun 2003 [1][2] dan Liao et al pada tahun 2017 [3].…”