Solar power integration in electrical grids is complicated due to dependence on volatile weather conditions. To address this issue, continuous research and development is required to determine the best machine learning (ML) algorithm for PV solar power output forecasting. Existing studies have established the superiority of the artificial neural network (ANN) and random forest (RF) algorithms in this field. However, more recent studies have demonstrated promising PV solar power output forecasting performances by the decision tree (DT), extreme gradient boosting (XGB), and long short-term memory (LSTM) algorithms. Therefore, the present study aims to address a research gap in this field by determining the best performer among these 5 algorithms. A data set from the United States’ National Renewable Energy Laboratory (NREL) consisting of weather parameters and solar power output data for a monocrystalline silicon PV module in Cocoa, Florida was utilized. Comparisons of forecasting scores show that the ANN algorithm is superior as the ANN16 model produces the best mean absolute error (MAE), root mean squared error (RMSE) and coefficient of determination (R²) with values of 0.4693, 0.8816 W, and 0.9988, respectively. It is concluded that ANN is the most reliable and applicable algorithm for PV solar power output forecasting.

This study was carried out to investigate the effect of the diamond-shaped Interlocking Chain Plastic Bead (ICPB) on fiber-reinforced fly ash-based geopolymer concrete. In this study, geopolymer concrete was produced using fly ash, NaOH, silicate, aggregate, and nylon66 fibers. Characterization of fly ash-based geopolymers (FGP) and fly ash-based geopolymer concrete (FRGPC) included chemical composition via XRF, functional group analysis via FTIR, compressive strength determination, flexural strength, density, slump test, and water absorption. The percentage of fiber volume added to FRGPC and FGP varied from 0% to 0.5%, and 1.5% to 2.0%. From the results obtained, it was found that ICBP fiber led to a negative result for FGP at 28 days but showed a better performance in FRGPC reinforced fiber at 28 and 90 days compared to plain geopolymer concrete. Meanwhile, NFRPGC showed that the optimum result was obtained with 0.5% of fiber addition due to the compressive strength performance at 28 days and 90 days, which were 67.7 MPa and 970.13 MPa, respectively. Similar results were observed for flexural strength, where 0.5% fiber addition resulted in the highest strength at 28 and 90 days (4.43 MPa and 4.99 MPa, respectively), and the strength performance began to decline after 0.5% fiber addition. According to the results of the slump test, an increase in fiber addition decreases the workability of geopolymer concrete. Density and water absorption, however, increase proportionally with the amount of fiber added. Therefore, diamond-shaped ICPB fiber in geopolymer concrete exhibits superior compressive and flexural strength.