The availability of solar irradiance time series without missing data is an ideal scenario for
researchers in the field. However, it is not achievable for a variety of reasons, such as …

To realize carbon neutrality, there is an urgent need to develop sustainable, green energy
systems (especially solar energy systems) owing to the environmental friendliness of solar …

The widespread use of data across various fields has made missing data imputation
technology a crucial tool. High-quality data is essential for effective energy management in …

Rare or missing data pose significant challenges in the prediction of wind power (WP) and
photovoltaic power (PV). Many methods address the data scarcity issue solely through …

This study proposes a modified gaps filling method, expanding the column mean imputation
method and evaluated using randomly generated missing values comprising 5%, 10 …

In this study, two innovative techniques were introduced, including data enrichment and
optimization, with the aim of significantly improving the accuracy of air over-pressure (AOP) …

Photovoltaic (PV) power is subject to variability, influenced by factors such as meteorological
conditions. This variability introduces uncertainties in forecasting, underscoring the …

Accurate solar resource forecasting is important because of the inherent variability
associated with solar energy and its significant impact on the cost for energy producers. The …

Solar photovoltaic (SPV) generators play a vital role in the global pursuit of sustainable
energy. However, the intermittent nature of SPV generators poses a challenge to power …

With recent advancements in data technologies, particularly machine learning, research
focusing on the enhancement of energy efficiency in residential, commercial, and industrial …