Quantifying Real-Time Experimental Data to Evaluate Statistical Model for the Solar Energy Irradiation Efficiency in Kuala Lumpur at the Highest and Lowest Relative Sunshine Duration in 2023
DOI:
https://doi.org/10.31185/wjes.Vol14.Iss2.862Keywords:
Solar Energy Irradiation, Meteorological Parameters, Statistical and Regression Model, Kuala Lumpur Region, Annual Highest/Lowest IrradiationAbstract
The experimental data is obtained in regional Kuala Lumpur region via three stages. First, develop a statistical model using real-world meteorological data, such as sunlight hours, precipitation/rainfall, and relative humidity, to identify the highest and lowest relative monthly sunshine durations. The solar panel was used to directly measure sun irradiation in June and November 2023, from 9:30 am to 4:30 pm. Checking the statistical model's accuracy is the final step. Research indicated that June had the highest relative sunshine duration (51.7%) and mean daily irradiance (≈619 W/m²), while November had the lowest (46.4%) and mean irradiance (≈560 W/m²). Temperature and day duration vary slightly across annual months, although moisture and cloud cover vary more significantly. The model-estimated decline from June to November (≈10.3%) matches the observed irradiance-based reduction (mean ≈9.4%, range 8.9-9.85%), indicating good agreement within experimental uncertainties. Smoothing the irradiance time series (5-10 min intervals) reduced absolute variability but did not significantly affect the relative decline between months (≈8.7-9.8%), making the comparison robust. Validation metrics and tests (KS and cross-validated regression diagnostics in Methods) confirm the statistical model's reliability for monthly assessments in this tropical urban setting. KL's monthly and seasonal rainfall fluctuations result in a 9-11% variation in solar energy between the driest and wettest months. This variability is significant in system design, financial planning, yield forecasts, reserve capacity planning, and battery size for rooftop and distributed PV. The provided regression model and real-time irradiance measurements agree within experimental error, suggesting that the model may be a helpful tool for monthly-scale PV yield estimate and planning when combined with additional local meteorological inputs and multi-site validation.
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