International Journal of Applied Science and Research

Sebahudin Kedir Seman 1 and Assoc. Prof. Buket Akdol Doyuran 2

DOI: https://doi.org/10.56293/IJASR.2025.6301

ABSTRACT: The study aims to adapt and validate a measurement tool for emotional wellness, specifically focusing on its multidimensional structure. The study was conducted by using online survey method with a sample size 356 white-collar employees from selected service sectors in Ethiopia. Primary data was gathered using a modified 16- item structured emotional wellness questionnaire. Exploratory Factor analysis (EFA) and confirmatory factor analysis (CFA) was conducted to test the validity of EWS. Cronbach alpha and item-total score correlation was used to analyse the reliability of the EWS. The EFA showed that the EWS has a two-factor structure, with 14 items accounting for 52.2% of the total variance, indicating a valid factor structure. The sub-dimensions were identified as "Emotional Management" and "Emotional Awareness,". Confirmatory Factor Analysis (CFA) supports the twofactor model, with excellent fit with various statistical indices (CIMN (X²/ df=1.86), RMSEA =0.052, SRMR = 0.049, CFI= 1.000, NFI= .996, GFI =.997 and RFI= .995). The scale's reliability is confirmed through the Cronbach Alpha internal consistency coefficient, with an overall score of α= 0.922 and with sub-dimensions scoring α= 0.897 and α= 0.864, respectively. The scale's unique features demonstrate high discriminatory power, with a satisfactory correlation with the items' index score and a range of 0.527 to 0.723. The finding shows that the EWS is a reliable and theoretically sound tool for assessing emotional wellness. This measurement instrument has been exclusively evaluated on white-collar employees within select service sectors in Ethiopia. It requires further testing across diverse cultures, sectors, and larger sample sizes to ensure generalisability.

Keyword: Emotional Wellness, Scale Adaptation, Validity, Reliability.

1 Kayode Owa, 2 Charles Akinropo

DOI: https://doi.org/10.56293/IJASR.2025.6302

ABSTRACT: Solar power, while abundant, is unpredictable due to its intermittency, causing instability in energy supply and imbalances between supply and demand, which can threaten grid reliability. To address these challenges, innovative solutions are required, especially as electricity demand fluctuates. Energy Storage Systems (ESS) help manage peak shaving and load shifting, but erratic energy sources can degrade batteries, leading to high costs. Accurate forecasts of energy consumption (EC) and solar energy generation (EG) are crucial for optimizing solar microgrids. This study evaluates deep learning models, including Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and a hybrid CNN-GRU model, to predict both EC and EG. A multi-input, parallel processing approach was used to capture temporal and spatial patterns for real-time applications with reduced data drift and improved accuracy. The model was tested using 47 months of historical data from the Sceaux microgrid near Paris, France, spanning from December 2006 to November 2010, from the University of California, Irvine repository. The data was also used to optimize the photovoltaic (PV) system sizing. The proposed method achieved excellent results for EG prediction with a Mean Absolute Error (MAE) of 3.974 and a Root Mean Squared Error (RMSE) of 6.603. For EC prediction, it obtained an MAE of 4.869, an RMSE of 6.527, and a Mean Absolute Percentage Error (MAPE) of 0.113, demonstrating its effectiveness for both short-term and long-term forecasting.

Keyword: Parallel Processing Approach, Solar Power Generation Prediction, Energy Consumption Prediction, Convolution Neural Network, Bidirectional Gated Recurrent Unit, Long-and-short term prediction.