Evaluation of the performance of the WRF model is carried out for simulating the surface winds and the diurnal cycle of wind speed for the small island developing state of Fiji at a 1.33 km by 1.33 km grid resolution using 1deg gridded data from NCEP-FNL. Simulations are performed for an austral summer (January 2017) and an austral winter (July 2017) month using the dynamical downscaling and the two-way nested approach. A set of physics parameterization schemes together with topo_wind = 1, 2 and ysu_topdown_pblmix = 1 physics settings associated with YSU PBL scheme are used to correct the surface winds and the diurnal cycle of wind speed. The results reveal that the WRF model is able to capture the surface winds and the diurnal cycle of wind speed on the windward side. Surface winds on the leeward side and the outer islands, show positive bias especially at nighttime for January and at both the day and night time for July. The statistical evaluation of all stations for January (July) showed a bias of 1.16 m/s (1.89 m/s), RMSE of 2.40 m/s (3.14 m/s), STDE of 1.88 m/s (2.08 m/s) and diurnal cycle correlation of 0.74 (0.68) using topo_wind = 2 and ysu_topdown_pblmix = 1.

This study carries out an analysis of the 10 MW Butoni wind farm in the tropical southwest Pacific island of Fiji using 6 years of uninterrupted near-surface wind observations (2013–2018). The standard wind-industry software, WAsP is used to analyse and evaluate the wind characteristics of the wind farm and the surrounding areas. The modelled and operational AEP are discussed with the related economic analysis together with the main causes for the under-performance of the wind farm. The results revealed that the mean wind speed, power density and the AEP at the Butoni wind farm are below the utility-scale standard of 6.4 m/s, 300 W/m² and 500 MWh/year/turbine respectively, at 55 m above ground level (AGL). The main reason for the under-performance of the wind farm is that it was commissioned for a low mean wind speed regime of Wind Power Class 1. The wind farm has a lower-than-expected capacity factor of 5.4% and a higher wind shear coefficient of 0.35. An economic analysis revealed that the payback time is 24.5 years, and the cost of energy generation is FJD $ 0.55/kWh.

This study presents an analysis of the proposed Weather Research and Forecasting–Wind Atlas Analysis and Application Program (WRF–WAsP) models coupling methodology and evaluates the improvement in the accuracy of the wind predictions for the small island developing state (SIDS) of Fiji in the tropical Southwest Pacific region. The results revealed that the proposed WRF–WAsP coupling methodology can be used as a wind resource assessment methodology provided very-high resolution dynamically downscaled wind resource data is available in the order of 10 years for utility-scale wind power applications. The analysis also revealed that the 1 km × 1 km WRF model data from the finest (high-resolution) domain (d03) is best for coupling WRF–WAsP for wind resource parameters evaluation. The coupling methodology of WRF–WAsP improved the accuracy of the wind prediction by 0.2–6% for the wind resource parameters evaluated for Fiji.

This study assesses and models the wind resources and energy potential of five smaller outer islands in Fiji that are not connected to the central electricity grid. Long-term wind data spanning approximately 9–11 years were analysed using the Wind Atlas Analysis and Application Program. High-resolution wind resource maps were generated and evaluated for each island site. Results indicated that Yasawa and Koro Islands have a higher wind potential, with mean wind speeds of 7.01 and 6.47 m s−1 and corresponding wind power densities of 335 and 339 W m−², respectively, indicating suitability for utility-scale wind power applications. The islands of Gau, Ovalau and Taveuni exhibit moderate wind speeds ranging from 4.26 to 4.82 m s−1, indicating potential for stand-alone or hybrid renewable energy systems. The modelled wind farms have installed capacities of 0.8–10 MW, yielding annual energy productions (AEPs) of 4.23–22.97 GWh yr−1. These AEPs are sufficient to meet the energy demand of the islands. The analysis highlights the spatial mismatch between island-wide grid-average wind resources and localized turbine micro-siting conditions, emphasizing the importance of site-specific assessment in complex island terrain. These findings support wind energy integration into Fiji’s renewable energy transition in outer islands without grid-connected electricity, align with Fiji’s UNFCCC nationally determined contribution targets and support sustainable development goals (SDGs) SDG 7 (affordable and clean energy) and SDG 13 (climate action).

The hospitality sector in Pacific Island Countries (PICs) is highly energy intensive and remains strongly dependent on imported fossil fuels for hot water production, leading to elevated operational costs and greenhouse gas emissions. This study presents a techno-economic-environmental assessment of a glazed flat-plate solar water heating (SWH) system for a representative 3-star hotel in Fiji. Using RETScreen Expert, multiple system configurations were simulated by varying the storage-to-collector area ratio to identify an optimal design. The selected system achieves a solar fraction of 45.5 % and delivers annual energy savings of approximately 9,996 kWh. Over a 20-year project lifetime, the system reduces cumulative greenhouse gas emissions by about 158 tonnes of CO₂. Financial analysis indicates strong economic viability, with a simple payback period of 7.7 years, an equity payback period of 4.7 years, and a net present value of FJD 15,151, achieved without subsidies. Model outputs were independently validated using the System Advisor Model (SAM), demonstrating close agreement between the two tools. The findings confirm that SWH systems represent a technically feasible, economically attractive, and environmentally sustainable solution for reducing fossil fuel dependence in Fiji’s hotel sector. The proposed system directly supports Sustainable Development Goals (SDGs) 7 and 13 and contributes to SDGs 8, 9, and 11 through improved energy efficiency, emissions reduction, and sustainable tourism infrastructure.