Daylighting can improve lighting energy efficiency and support health-related indoor environmental quality. However, existing single lighting strategies often struggle to simultaneously provide stable daylight supply, personalized control, and maximized energy savings. To address this challenge, integrating controllable daylight delivery with workstation-level lighting control provides a potential pathway. Building on a previously developed freeform fiber-optic daylighting system (FFODS), this study investigates its building-level application under non-uniform window daylight and auxiliary electric lighting. A workflow integrating optical simulation, Radiance-based daylight simulation, and parametric superposition analysis was applied to a reference open-plan office. The simulations evaluated workstation illuminance, EML-based non-visual lighting potential, and electric-lighting energy-saving potential. In the basic case, the predicted annual 500 lux satisfaction rate averaged over the 24 workstations increased from 18.8% to 54.4%. Using a previously measured fiber-emission spectrum as a representative input, the predicted number of days satisfying the adopted EML criterion at deep-zone workstations increased from 0 to 230. Among evaluated schemes, FFODS combined with zoned dimming control achieved the lowest energy consumption. Greater fiber-allocation flexibility reduced the system capacity required for a given lighting-satisfaction level, while strategy benefits depended on the spatiotemporal pattern of workstation illuminance deficits. System performance was mainly governed by direct solar resource availability and constrained by diminishing marginal returns from passive daylighting. The results identify spatiotemporal supply-demand mismatch as an important constraint on system utilization, providing design insights for system configuration, allocation control, and integration with auxiliary electric lighting.
Estimating battery state of health (SOH) from real-world electric vehicle data is difficult because SOH cannot be measured directly and only limited data are available during the early stage of vehicle operation. Using approximately three years of operating data from 100 Changan electric vehicles, this study investigates SOH estimation and early-data requirements. Reference SOH was derived from charging records using ampere-hour integration, repeated random subinterval estimation, median absolute deviation (MAD)-based outlier detection, and Kalman filtering. Features were extracted from incremental capacity analysis (ICA), cell-voltage and temperature inconsistency, and vehicle operating conditions, and Mamba was used to model degradation across successive cycles. One benchmark vehicle was used to determine the feature set, fixed network architecture, and common modeling settings. The resulting modeling scheme was then applied to the other 99 vehicles, with key hyperparameters selected separately using validation data. For each vehicle, early-life samples ranging from 5% to 40% were used for training, the following 10% for validation, and the remainder for testing. On the benchmark vehicle, Mamba achieved a root mean square error (RMSE) of 0.0777, a mean absolute error (MAE) of 0.0618, and a mean absolute percentage error (MAPE) of 8.10%, outperforming the comparison models. For the other 99 vehicles, training with the first 30% of samples yielded mean RMSE, MAE, and MAPE values of 0.0564, 0.0442, and 5.28%, respectively. Errors decreased as more early data were used, with a clear improvement from 10% to 15% and limited gains beyond approximately 30%. These results provide a reference for selecting the proportion of early data used in SOH modeling for electric vehicles of the same model.
Ocean renewable energy resources have driven zero-emission targets for coastal communities. Integrating electric vehicles (EVs) into distant coastal buildings has further enabled effective energy-sharing and energy-flexibility management solutions. Existing studies related to EV-integrated buildings lack in-depth optimal solutions that consider both EV charging decision-making and building performance. Furthermore, there is a lack of a multi-objective optimisation framework for coastal zero-emission buildings that simultaneously incorporates impact on the utility grid, wave attenuation and road traffic. This study designs a distant energy sharing network via EVs involving wave energy converters (WEC) and floating photovoltaics (FPV) with peak shaving controls and EV smart charging. Using an integrated approach of TRNSYS and NSGA-II, this study conducts multi-objective optimisation of the energy sharing network to configure renewable energy and energy flexibility resources to balance various objectives, including wave attenuation improvement, traffic impact mitigation, and peak demand reduction. Through optimal design and control, when the hotel building is equipped with 10% WEC and 90% FPV, and the office building is fully supported by FPV, with a 500-kWh office battery and a 1000-kWh hotel battery, NPVrel is improved by 13.74 ×106 HKD, and the negative road traffic impact is reduced from 0.161 to 0.082. Meanwhile, the system can obtain an improved wave attenuation impact from 0 to 0.009. The detailed analysis of the Pareto front provides a cluster of case-specific design recommendations for the proposed EV-integrated ocean energy exchange system, facilitating effective decision-making for the stakeholder to achieve multiple objectives for the grid, traffic and coastal sides.
5G base stations cabinet-level variable-speed cooling systems can flexibly adjust multiple operating parameters to match cooling capacity with thermal load. However, these systems encompass the variable-speed loop thermosyphon system (VLTS), the variable-speed vapor compression refrigeration system (VVCS), and the variable-speed hybrid cooling system combining thermosyphon and vapor compression (VHSTV), particularly the VHSTV, which exhibits strong coupling among multiple operating parameters, making collaborative optimization difficult, limiting energy efficiency improvements. To address these challenges, this study proposes an easy-to-operate and universal multi-parameter sequential speed reduction (MSSR) optimization strategy, which utilizes the IT equipment outlet temperature as the control parameter, while ensuring thermal safety, to collaboratively optimize compressor speed, evaporator fan speed, and condenser fan speed. The performance of the three systems under the MSSR and conventional on-off control (OOC) strategy was comparatively analyzed through experiments to evaluate the effectiveness of the MSSR strategy and identify the optimal cooling system under an outdoor ambient temperature of 20°C and a cabinet thermal load of 6 kW. Results show that the EER of the VLTS under the MSSR strategy reaches 12.20, while the EER of the VVCS and VHSTV under the MSSR strategy reaches 7.35 and 12.72, representing improvements of 40.9%-62.6% and 22.9%-44.2% over the OOC strategy. Under the experimental conditions, VHSTV is the optimal cooling system under the MSSR strategy, which indicates that activating the compressor while reducing fan speeds can improve the system's EER. Compared to enumeration-based optimization, the MSSR strategy reduces the experimental workload required for VLTS, VVCS, and VHSTV optimization by at least 71.4%, 93.9%, and 93.9%. The proposed strategy effectively enhances system performance and offers methodological guidance and practical insights for cooling systems optimization.
Substations are fundamental components for the efficient operation of district heating networks. Cascaded layouts are widely recognized as effective solutions for enhancing substation temperature performance, thereby improving overall network efficiency. Despite their potential, clear and quantitative criteria for their design and operation remain lacking, particularly regarding their effectiveness in future low-temperature networks. Considering the renewed interest in district heating, this study investigates the thermal performance of a cascaded two-stage substation applied to two building typologies across six European climates. Using the average return temperature and the annual volume of water withdrawn from the network as key performance indicators, the results are compared with those of a conventional parallel layout. Results show that the cascaded configuration systematically reduces both the return temperature and the network water withdrawal, with benefits strongly dependent on climatic conditions and the relative share of space heating and domestic hot water in the building load. For the apartment building, reductions in the seasonal return temperature range from 0.9–1.2 °C in cold and continental climates to 4.7 °C in zones with milder winters. Corresponding reductions in annual water withdrawal range from 4.47% to 12.6%. For the office building, the reductions are smaller in cold climates (0.4–0.6 °C and 2.62% to 3.44%) due to the predominance of space heating demand over domestic hot water production. Sensitivity analyses on the size of the low-temperature heat exchanger for domestic hot water show that, in northern climates, performance improvements saturate once its heat transfer area becomes comparable to that of the high-temperature exchanger, with further enlargement yielding only marginal benefits below 1%. Conversely, substantially larger gains are achieved in southern regions. Under the assumption of a fixed total heat transfer area, allocating approximately 50% of this area to the low-temperature stage is identified as the most effective solution. Finally, the transition to lower operating temperatures leads to a slight reduction in the effectiveness of the two-stage configuration, with a more pronounced impact in mild climates. However, the two-stage configuration still outperforms the parallel layout.
