Effects of high humid weather conditions on photovoltaic (PV) modules were examined in this study, particularly insulation resistance. Three types of tests were conducted which include leakage voltage test, leakage current test, and wet leakage current test. Due to the usual field constraints in the study of insulation resistance, which limited measurements of leakage current, assurance was made that representative sampling were conducted. The study found a high number of modules with low insulation resistance and high leakage voltage values, which can interrupt the PV plant operation. High leakage voltage creates safety hazards issues. About two third of the samples, which showed deep moisture ingress in the modules, could not pass the minimum criterion of IEC 61646 standard for wet insulation resistance testing. The leakage current results showed the same trend as of leakage voltage, proving that leakage voltage test, which is quite easy and economical, can be used to detect such type of problems in field tests. Prolonged humidity conditions of the PV power plant particularly from natural disaster, should be avoided. Efficient drainage system should be supported in and around installations and all other moisture sources should be regularly removed from the plant area to keep proper operation and minimize losses in energy production.

Electricity load forecasting is an important aspect of power system management. Improving forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings. Often, collected data consist of Missing Values (MVs), anomalies, outliers, or other inconsistencies caused by power failures, metering errors, data collection errors, hardware failures, network failures, or other unexpected events. This study uses real-world data to investigate the possibility of using synthetically generated data as an alternative to filling in MVs. Three datasets were created from an original one based on different imputation methods. The imputation methods employed were linear interpolation, imputation using synthetic data, and a proposed hybrid method based on linear interpolation and synthetic data. The performance of the three datasets was compared using deep learning, machine learning, and statistical models and verified based on forecasting accuracy improvements. The findings demonstrate that the hybrid dataset outperformed the other interpolation methods based on the forecasting accuracy of the models.

In this paper, we present a model for calculating the State of Health (SOH) of battery energy storage systems (BESSs) and battery capacity percentage, specifically tailored for grid-scale applications in Thailand. Unlike conventional models that rely on controlled laboratory data, the proposed approach uses actual operating temperature data for both development and validation, enabling a more correct assessment of battery performance under the high-temperature conditions typical of tropical climates. A set of coefficients derived from real operating data was incorporated, and the SOH results deviated by only 0.05% from theoretical values, proving high predictive accuracy beyond laboratory settings. Our findings revealed that capacity degradation rates in Thailand are approximately 20–60% higher than under the best conditions. Over a 10-year warranty period, battery capacity declined to approximately 80% at the lowest temperature range, 60% at the average range, and 40% at the highest range. By calculating both SOH and remaining capacity, the model provides a practical tool for lifespan prediction and system planning. Based on these findings, it is recommended that thermal management systems support battery operating temperatures between 25 and 35 °C and limit cooling losses below 10%, thereby mitigating energy yield degradation and ensuring efficient BESS operation. These results highlight the importance of incorporating real environmental data into degradation modeling. Future studies should include long-term monitoring of operating temperatures and cooling demand, economic analyses to enhance operational efficiency, and evaluation of external heat loads, particularly from solar radiation, to further refine predictions for tropical climates.