A one-minute resolution global horizontal irradiance dataset recorded in Malaysia for photovoltaic system modelling
Chun Lim Siow1, Chee Wern Hew2, Ousama M T Ajami2
1Centre for Electric Energy and High Voltage Engineering, COE Robotics & Sensing Technologies Multimedia University, Cyberjaya, Malaysia.
Abstract:
High resolution solar irradiance data are essential for the reliable modelling and validation of solar photovoltaic (PV) systems. This article presents a dataset of one-minute resolution Global Horizontal Irradiance (GHI) measurements collected at the UCSI University, Kuala Lumpur Campus, Malaysia throughout 2025. The standalone GHI measurement system was powered by a solar PV panel and battery. It utilised a Kipp & Zonen CMP3 pyranometer, an ISO 9060 spectrally flat class C thermopile pyranometer. To ensure precise data logging, the pyranometer's analogue signals were digitalised using an ADS1115 16-bit analogue-to-digital converter (ADC) with a programmable gain amplifier. An ESP32-WROOM-32U microcontroller was used to process these signals, compute the irradiance measurements, and transmit the data to a ThingSpeak Internet of Things (IoT) server at a one-minute intervals from 07:00 to 18:59 local time. A total of 259,858 observations were retrieved out of the 262,800 expected observations. The 2942 missing observations (1.12%) were linear interpolated. Additionally, a quality control screening was conducted using Baseline Surface Radiation Network tests based on Extremely Rare Limits and Physically Possible Limits, with no observations flagged. The dataset has a combined standard uncertainty of ±4.42% and an expanded standard uncertainty of ±8.84% ( ). This one-minute resolution GHI dataset captures rapid irradiance variations in Kuala Lumpur, Malaysia. The dataset contained daytime GHI measurements only and does not include other meteorological variables. It may support PV system modelling, simulation, and validation by enabling representation of irradiance-driven power fluctuations. The data can also be used for short-term GHI and PV power forecasting, including machine learning-based approaches. In addition, the dataset may support studies of PV power variability, PV battery energy storage system scheduling, PV output smoothing, and grid integration under high PV penetration.
