Health, Safety, Environment, and Ergonomics Analysis of Solar Power Systems Using an Adaptive Neuro-Fuzzy Inference System

Solar energy is recognized as a clean energy source; however, its implementation presents various challenges related to Health, Safety, Environment, and Ergonomics (HSEE) aspects that must be addressed. This study aimed to identify subvariables within the HSEE aspects and to analyze HSEE assessments using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The identification results indicate that the health aspect comprises two sub-variables: heat stress and toxic materials. The safety aspect includes three sub-variables: electrical risk, fire hazard, and fall risk. The environmental aspect comprises three sub-variables: ecosystem damage, land use, and material recycling. The ergonomics aspect includes three sub-variables: musculoskeletal injury risk, work posture, and manual handling risk. The ANFIS model was developed from questionnaire data categorized into three risk assessment levels: good, fair, and poor. Model performance was evaluated using the Root Mean Square Error (RMSE) as an indicator of accuracy during both the training and testing phases. The evaluation results show RMSE values for the health variable of 0.0120 (training) and 0.0512 (testing); safety of 0.0232 (training) and 0.1515 (testing); environment of 0.0158 (training) and 0.0548 (testing); and ergonomics of 0.0294 (training) and 0.0327 (testing). The overall RMSE values for the health, safety, environment, and ergonomics models were 0.034, 0.140, 0.045, and 0.025, respectively. This study demonstrates that the ANFIS method can serve as a decision-support tool for systematically and adaptively assessing HSEE performance, thereby improving the health, safety, environmental, and ergonomic aspects of solar power plants.