Research Publications

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Open Access
Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction
(Elsevier Ltd, 2026-10-01) Perera, M; De Hoog, J; Bandara, K; Weeratunge, H; Halgamuge, S
Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this forecasting task difficult. To address this, using cloud images, which capture the second-to-second changes in cloud cover affecting solar generation, has shown promise. Recently, deep neural networks with attention that focus on important regions of an image have been applied with success in many computer vision applications. However, whether such methods provide meaningful benefits for cloud movement forecasting, and how such improvements propagate through to downstream solar generation forecasting accuracy, remains under-explored. In this study, we conduct a large-scale empirical investigation of the impact of attention-based cloud forecasting on solar generation forecasting, addressing a gap that has been overlooked in the literature. To this end, we develop a pipeline that incorporates an attention-enhanced convolutional long short-term memory network and an existing self-attention-based video prediction method to forecast cloud movement using satellite imagery. The effectiveness of the resulting cloud forecasts is evaluated through their downstream impact on solar forecasting across 50 PV sites in Australia. We further provide insights into the cloud conditions under which attention-based cloud forecasting methods yield the most significant improvements in downstream solar forecasting accuracy. We find that for clouds at high altitudes, the cloud predictions obtained using attention-based methods result in solar forecast skill score improvements of 5.86% or more compared to non-attention-based methods.
Open Access
Unpacking how the Living Arrangements of Undergraduates Influence Quality of Life
(School of Psychology. Faculty of Humanities and Sciences, SLIIT, 2025-10-10) Bandara, K; Abeysekara, M; Vihangi, S; Perera, S; Jayasekara, S; Samaratunge, T; Goonetilleke, N
This study examined the connection between undergraduate students' living arrangements (private vs rented accommodation) and their Quality of Life (QoL) at the Sri Lanka Institute of Information Technology (SLIIT). All four domains of quality of life, psychological well-being, physical health, environmental factors, and social relationships were measured using an adapted version of the World Health Organisation Quality of Life: Brief Version (WHOQOL-BREF). The cross-sectional studyincluded a sample of 64 individuals obtained from the campus premises between the ages of 18-25. Multivariate Analysis of Variance (MANOVA) revealed no statistically significant differences in QoL dimensions based on accommodation type. However, the effect sizes indicate living arrangements to be a better predictor of the environmental factors as opposed to other domains of QoL. Furthermore, a chi-square test yielded a strong association between the year of study and living arrangements among students, suggesting that the year of study may have an impact on students’ choice of accommodation. These results further demonstrate the diversity of QoL and imply that, although environmental influences are worthy of consideration, living arrangements might not be a strong factor to explain students’ well-being. While the nature of the sample (i.e., small and convenient) may have hindered the statistical significance of the study, the present findings highlight the necessity for subsequent studies to accurately uncover the impact of student life, accommodation, and other related factors onthe quality of life.

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