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The world's oceans, covering approximately 71 % of the Earth's surface, harbor vast wave energy resources, offering a potential solution to the pressing energy crisis and environmental pollution caused by fossil fuel combustion. In recent years, there has been a global surge in exploration and development of wave energy conversion technologies, aimed at effectively harnessing wave energy to realizing sustainable and intelligent sea solutions. This comprehensive review examines the advancements, challenges and future research directions of current mainstream wave energy conversion technologies. Firstly, the distribution of global wave resources and energy conversion process involved in wave energy extraction are analyzed. Subsequently, various wave energy conversion technologies are meticulously classified based on their power take-off systems, and the strengths and challenges of each category are comprehensively investigated. Especially, a universal standard consisting of 5 key indicators has been established to evaluate and compare the characteristics of various wave energy conversion technologies based on different transduction mechanisms, providing comprehensive and intuitive valid references for developers with different needs. The evaluation reveals that the wave energy converters based on hybrid systems demonstrate significant promise as conversion technologies. Moreover, the review presents a summary and analysis of the latest advancements in the application of artificial intelligence within wave energy conversion technologies. This emerging integration of artificial intelligence showcases promising development and potential for further enhancing wave energy conversion systems. Lastly, the review explores the application and future research directions of wave energy conversion technologies. Notably, the investigation highlights the potential of developing a multi-energy complementary power generation system that can concurrently harness multiple renewable energy sources coexisting at sea. This concept represents a promising avenue for future research and development.

Enhancing the energy efficiency of buildings significantly relies on monitoring indoor ambient temperature. The potential limitations of conventional temperature measurement techniques, together with the omnipresence of smartphones, have redirected researchers' attention towards the exploration of phone-based ambient temperature estimation methods. However, existing phone-based methods face challenges such as insufficient privacy protection, difficulty in adapting models to various phones, and hurdles in obtaining enough labeled training data. In this study, we propose a distributed phone-based ambient temperature estimation system which enables collaboration among multiple phones to accurately measure the ambient temperature in different areas of an indoor space. This system also provides an efficient, cost-effective approach with a few-shot meta-learning module and an automated label generation module. It shows that with just 5 new training data points, the temperature estimation model can adapt to a new phone and reach a good performance. Moreover, the system uses crowdsourcing to generate accurate labels for all newly collected training data, significantly reducing costs. Additionally, we highlight the potential of incorporating federated learning into our system to enhance privacy protection. We believe this study can advance the practical application of phone-based ambient temperature measurement, facilitating energy-saving efforts in buildings.

This paper proposed a portable balloon-integrated photovoltaic system (BIPVS) deployed at low altitude. The inflatable and deflatable design enhances the proposed system flexibility and mobility, enabling it have a wider range of application scenarios. Case studies were conducted based on cities' data of Vasteras, Vancouver, New York, Shanghai and Hong Kong to evaluate 10,000 BIPVS's annual power generation potential. Mid-to-high latitudes are not suitable for photovoltaic power generation in winter due to snow and ice coverage. Excluding the unsuitable winter months, simulation results show that the average monthly power generation of the BIPVSs amounts to 3.921 GWh, 4.238 GWh, 4.275 GWh, 3.337 GWh, and 3.379 GWh, respectively, during the effective working months within a year, which shows the superior performance of mid-to-high latitudes over their low latitudes. Over the life cycle, the BIPVSs exhibit a cumulative power generation capacity, amounting to 479.492 GWh, 592.18 GWh, 672.105 GWh, 641.155 GWh, and 708.334 GWh, respectively, and their total profits are 79.614 million USD, 37.007 million USD, 75.146 million USD, 12.946 million USD, 107.369 million USD, accompanied by the return on investment of 218.6 %, 101.6 %, 206.3 %, 35.5 %, 294.8 %, respectively. These findings illustrate the significant energy and economic advantages and potential of BIPVS.

Self-powered smart buoys are widely used in sustainable sea, such as marine environmental monitoring. The article designs a self-powered and self-sensing point-absorber wave energy converter based on the two-arm mechanism. The system consists of the wave energy capture module, the power take-off module, the generator module and the energy storage module. As the core component of the wave energy converter, the power take-off module is mainly composed of a two-arm mechanism, which can convert the oscillation heave motion into unidirectional rotary motion. To evaluate the power generation performance of the system, the kinematic and dynamic models of the wave energy converter with the flywheel are established, and the disengagement and engagement phenomena of the flywheel are analyzed. The effectiveness of the prototype in capturing wave energy is verified through dry experiments in lab and field tests. The dry experiment reveals that the maximum output power of the system is 5.67 W, and the maximum and average mechanical efficiency are 66.63 % and 48.35 %, respectively. Additionally, the field test demonstrates that the peak output power can reach 92 W. Meanwhile, the generated electrical signals can be processed by deep learning algorithms to accurately identify different wave states. This high performance confirms that the proposed wave energy converter can meet its own energy needs by capturing wave energy in the marine environment, while also achieving self-sensing for wave condition monitoring. The system has great potential for promoting the development of intelligent sustainable sea in the future. 

Prioritizing electric power system decarbonization is crucial for meeting global carbon neutrality targets. However, the role of energy technology cost reduction driven by innovation in advancing carbon neutrality in the electric power system has not been well studied. To fill this gap, an integrated investment planning and operation model is developed to simulate the carbon neutral pathway in the electric power system over a 30-year period from 2020 to 2050. The learning curves with different learning rates are incorporated into the model to represent different energy technology innovation scenarios. According to our results, the advanced innovation scenario is projected to achieve carbon neutrality in the electric power system five years earlier compared with the conservative innovation scenario, with a cost savings of 465 billion CNY. In addition, both inter-regional and intra-regional collaboration facilitate the achievement of carbon neutrality in the electric power system at a reduced cost. 

Vibration energy harvesting technology is characterized by wide distribution, is pollution-free and independent of weather and climate, and is suitable for powering low-power sensors to ensure efficient and safe operation in freight railroads. This paper proposed an electromagnetic vibration energy harvester based on a series coupling input mechanism for the self-powered sensors in freight railroads. The design utilizes only one rack for vibration energy input to minimize the moment acting on the vibration source during the working process. Two pinions meshed with the rack convert the up and down vibrations into a two-way rotation. The one-way bearings and another pair of gears convert the opposite rotations of two parallel shafts into one-way rotation of the generator shaft, generating electricity. Supercapacitors and rectifier voltage regulator modules are utilized to store electrical energy efficiently. A peak power of 10.219 W and maximum mechanical efficiency of 64.31% is obtained in the experiment equipped with a flywheel under the 8 mm-4 Hz sinusoidal vibration excitation. The experimental results showed that the flywheel can enable the proposed harvester to achieve better power generation performance when the amplitude and frequency are relatively high. 

Indoor air pollution resulting from volatile organic compounds (VOCs), especially formaldehyde, is a significant health concern needed to predict indoor formaldehyde concentration (Cf) in green intelligent building design. This study develops a thermal and wet coupling calculation model of porous fabric to account for the migration of formaldehyde molecules in indoor air and cotton, silk, and polyester fabric with heat flux in Harbin, Beijing, Xi'an, Shanghai, Guangzhou, and Kunming, China. The time-by-time indoor dry-bulb temperature (T), relative humidity (RH), and Cf, obtained from verified simulations, were collated and used as input data for the long short-term memory (LSTM) of the deep learning model that predicts indoor multivariate time series Cf from the secondary source effects of indoor fabrics (adsorption and release of formaldehyde). The trained LSTM model can be used to predict multivariate time series Cf at other emission times and locations. The LSTM-based model also predicted Cf with mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) that fell within 10%, 10%, 0.5, 0.5, and 0.8, respectively. In addition, the characteristics of the input dataset, model parameters, the prediction accuracy of different indoor fabrics, and the uncertainty of the data set are analyzed. The results show that the prediction accuracy of single data set input is higher than that of temperature and humidity input, and the prediction accuracy of LSTM is better than recurrent neural network (RNN). The method's feasibility was established, and the study provides theoretical support for guiding indoor air pollution control measures and ensuring human health and safety.

Photovoltaic noise barriers (PVNBs) offer a dual advantage of reducing traffic noise pollution and providing renewable electricity to cities. However, how the effective integration of PVNB-generated power into urban energy networks remains a critical area lacking research. To bridge this gap, this study proposes PVNBs-energy storage (ES)-charging station (CS; PVNBs-ES-CS) strategy. It can facilitate the actual consumption of PVNBs power and the mitigation the burden on the grid posed by electric vehicles (EVs) charging demands. The case study conducted in Guangzhou, China, reveals that PVNBs can support up to 5% of the total power demand for EVCSs. Under the PVNBs power maximization consumption scenario, PVNBs can meet up to 30% of the power demands from 60 EVCSs, with 58% of PVNBs generated power being consumed. In the PVNBs-ES-CS future utilization scenario, up to 30% of the power demand of 125 EVCSs can be met, and 36% of the power of PVNBs can be consumed. The combination of PVNBs and EVCSs offers a practical solution for incorporating renewable energy sources into urban energy networks. This application mode can be applied in various cities with EV demands and PVNB power generation data.

The flexible distribution network presents a promising architecture to accommodate highly integrated distributed generators and increasing loads in an efficient and cost-effective way. The distribution network is characterised by flexible interconnections and expansions based on soft open points, which enables it to dispatch power flow over the entire system with enhanced controllability and compatibility. Herein, we propose a multi-resource dynamic coordinated planning method of flexible distribution network that allows allocation strategies to be determined over a long-term planning period. Additionally, we establish a probabilistic framework to address source-load uncertainties, which mitigates the security risks of voltage violations and line overloads. A practical distribution network is adopted for flexible upgrading based on soft open points, and its cost benefits are evaluated and compared with that of traditional planning approaches. By adjusting the acceptable violation probability in chance constraints, a trade-off between investment efficiency and operational security can be realised.

Industrial parks play a crucial role as a carrier of industrial clusters and energy consumption. Accurately predicting the energy demand and carbon emissions trend is key to scientifically determining the pathways for low-carbon industrial parks. However, exploration in carbon emission prediction on industrial park scale is still in its infancy stage. This paper investigates fuel demand and carbon emissions from 2021 to 2035 in an industrial park in Jiangsu Province, utilizing the Long-range Energy Alternative Planning (LEAP) model to explore the pathways for low carbon development. Energy-saving and emission-reduction effects of different macro-economic policies and micro-energy planning are analyzed based on the energy balance and emission factor methods. Four scenarios are compared: the baseline scenario (BAS), green development scenario (GDS), low carbon scenario (LCS), and strength low carbon scenario (SLS). Results indicated that energy demand under BAS reached at 31.37 Mtce in 2035, and energy-saving rates of GDS, LCS, and SLS in 2035 were 12.94%, 14.00% and 19.08%, respectively. Carbon emissions reached 53.96 MtCO2e in BAS of 2035. However, in the same year, emissions decreased by 24.88%, 43.09%, and 52.52% in GDS, LCS, and SLS, respectively, with SLS being the most suitable for the park.