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Reducing the dependence on fossil fuels and utilizing the renewable energy have become essential due to the global resource exhaustion and unfriendly environmental impact from coal, petroleum and natural gas. Therefore, the rising attention has been paid to wave energy characterized by sustainability, clean, high energy density and extensive distribution. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to forecast accurately due to the complex marine condition and ubiquitous presence of chaos in nature. In this research, a novel hybrid model called STL–CNN–PE which combines seasonal-trend decomposition procedure based on loess (STL) and one-dimensional convolutional neural networks (CNN) with positional encoding (PE) was proposed to forecast SWH efficiently and accurately. To evaluate the proposed model comprehensively, the hourly standard meteorology data at station 44007, 46087 and 51000 from NOAA’s National Data Buoy Center were selected for model training and testing. The experimental results indicated that STL–CNN–PE provided more reliable forecasting values than the single model. Meanwhile, STL–CNN–PE had enormous advantage on speed and similar precision compared with EMD-LSTM. Finally, the experimental results revealed that the models provided better forecasting metrics at deeper waters.

For the survival and development of ‘One Belt, One Road’, the present work aimed to evaluate the current situation of wave energy resources around Sri Lankan (SL) waters. Thirty-year ERA-Interim wind data were used to drive the third-generation wave model WAVEWATCH-III, and the seasonal and regional distribution characteristics of wave energy resources in SL waters were analyzed. Furthermore, the optimal season and region that contribute most to wave power in the study area were determined. On the basis of 30-year hindcast wave data, the significant wave height and wave power density, the occurrence of available SWH and rich WPD, the effective storage of wave energy, and the contribution and stability of wave energy were also analyzed. Results show that extremely optimistic wave energy resources are found at the western, southern, and southeastern waters of SL; moreover, the period of June, July, August (JJA) has great advantages in terms of the overall level of WPD, wave energy effective storage, and the contribution rate of wave energy. In addition, the wave energy during JJA is more stable than that of other periods and thus is beneficial to the transformation and development of wave energy. This study also provides important guiding value for disaster prevention and reduction, coastal zone management, and coastal development in the crucial region of the 21st Century Maritime Silk Road.

Short-term forecasts of wave energy play a key role in the daily operation, maintenance planning, and electrical grid operation of power farms. In this study, we propose a short-term wave energy forecast scheme and use the North Indian Ocean (NIO) as a case study. Compared with the traditional forecast scheme, our proposed scheme considers more forecast elements. In addition to the traditional short-term forecast factors related to wave energy (wave power, significant wave height (SWH), wave period), our scheme emphasizes the forecast of a series of key factors that are closely related to the effectiveness of the energy output, capture efficiency, and conversion efficiency. These factors include the available rate, total storage, effective storage, cooccurrence of wave power-wave direction, co-occurrence of the SWH-wave period, and the wave energy at key points. In the regional nesting of numerical simulations of wave energy in the NIO, the selection of the southern boundary is found to have a significant impact on the simulation precision, especially during periods of strong swell processes of the South Indian Ocean (SIO) westerly. During tropical cyclone ‘VARDAH’ in the NIO, as compared with the simulation precision obtained with no expansion of the southern boundary (scheme-1), when the southern boundary is extended to the tropical SIO (scheme-2), the improvement in simulation precision is significant, with an obvious increase in the correlation coefficient and decrease in error. In addition, the improvement is much more significant when the southern boundary extends to the SIO westerly (scheme-3). In the case of strong swell processes generated by the SIO westerly, the improvement obtained by scheme-3 is even more significant.

Research on the diffusion characteristics of swells contributes positively to wave energy forecasting, swell monitoring, and early warning. In this work, the South Indian Ocean westerly index (SIWI) and Indian Ocean swell diffusion effect index (IOSDEI) are defined on the basis of the 45-year (September 1957–August 2002) ERA-40 wave reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the impact of the South Indian Ocean westerlies on the propagation of swell acreage. The following results were obtained: 1) The South Indian Ocean swell mainly propagates from southwest to northeast. The swell also spreads to the Arabian Sea upon reaching low-latitude waters. The 2.0-meter contour of the swell can reach northward to Sri Lankan waters. 2) The size of the IOSDEI is determined by the SIWI strength. The IOSDEI requires approximately 2–3.5 days to fully respond to the SIWI. The correlations between SIWI and IOSDEI show obvious seasonal differences, with the highest correlations found in December–January–February (DJF) and the lowest correlations observed in June–July–August (JJA). 3) The SIWI and IOSDEI have a common period of approximately 1 week in JJA and DJF. The SIWI leads by approximately 2–3 days in this common period.

Based on 28 years of hindcast data, this paper describes in detail the long-term variation trend of wave power density in the South China Sea. WAVEWATCH-III (WW3), the third generation wave computing model, which plays an important role in wave prediction. WW3 was used for inverse calculation of wave parameters, with spatial resolution of 0.25° × 0.25° and time resolution of 6h. The validity of the data was verified by three typhoon processes and the significant wave height data of the satellite altimeter in 2010. According to 28-year(1990-2017) hindcast data set, the author respectively mapped the South China Sea mean monthly, seasonal, annual wave power density (WPD) and the change trend diagram, including the long-term trend of WPD Mann-Kendall (MK) test, come the following conclusions: (1) Influencing the area WPD change trend of the dominant increasing season is winter (DJF), in which season average growth of WPD is about 1kW/m, and the largest waters is closed to 3kW/m. (2)The dominant growing month for WPD is in December, by compared with the before and next month in the largest growing sea area, it shows an increase and decrease trend of 10kW/m and 20kW/m respectively. (3)MK test results indicate that the long-term change of WPD keep on an increasing trend, but it is not significant, among which WPD had an increased mutation in 2000. (4)The dominant sea area is around Luzon Strait-Taiwan Strait- D.S Islands (116°E, 21°N). The average growth of WPD among these area in 28 years are about 0.15-0.25(kW/m)/yr.

At home and abroad, this chapter carries out the first detailed investigation on the climate characteristics of wind energy resource along the Maritime Silk Road. A series of key parameters are defined: availability of wind energy (effective wind speed occurrence, EWSO), available level occurrence (ALO, occurrence of WPD greater than 100 W/m2) moderate level occurrence (MLO, occurrence of WPD greater than 150 W/m2) rich level occurrence (RLO, occurrence of WPD greater than 200 W/m2) excellent level occurrence (ELO, occurrence of WPD greater than 300 W/m2) superb level occurrence (SLO, occurrence of WPD greater than 400 W/m2), to describe the availability and richness of wind energy. Systematically considering the wind power density, energy availability, energy level occurrences, energy stability, monthly and seasonal variability of wind energy, energy storage, the temporal-spatial distribution characteristics of wind energy along the Maritime Silk Road are analyzed, to provide theoretical support for the energy classification, short-term forecast and long-term projection of wind energy.

The research on medium and long-term forecasting of wind energy is still extremely scarce until now, although this work is closely related to the medium and long-term planning of wind energy development and power dispatching. This chapter used the CMIP data to carry out the long-term wind energy projection along the Maritime Silk Road for the next 40 years, which includes a series of key indicators, such as: the wind power density, energy availability, energy level occurrences, energy stability, monthly and seasonal variability, energy storage for the next 40 years, to provide reference for the long-term planning of wind energy utilization.

Currently, the studies on the wind power along the Road area are still scarce, let alone those on its climatic trends. However, these studies are the theoretical basis which is crucial to improving the accuracy of the mid- and long-term forecast and could further impact the mid- and long-term planning of wind power exploitation. Besides, nowadays there is abundant research on the evolution trends of the meteorology and ocean, but little on that of the wind power. In this chapter, the annual trend, and regional and seasonal difference of the trends of a series of key indicators of wind energy resource are calculated and analyzed for the first time, to provide a theoretical foundation for the improvement of medium – and long-term projection capacity for wind energy projection.

Currently, the marine and meteorological raw data are relatively abundant. However, the offshore wind energy data is scarce. How to extracted the useful information about wind energy development from the original big data with large volume and low information density, and then building a wind energy resource dataset has become the key support to the industrialization and efficient deployment of wind energy. It is also a common challenge for global colleague. In this chapter, the first open-ended and non-profit dataset of spatial-temporal characteristics of wind energy resource for the Maritime Silk Road at home and abroad was established, to provide scientific reference and data support for offshore wind energy resource evaluation. A wind energy resource, which should include the temporal-spatial characteristics, energy classification, climatic variation of wind energy, long-term projection of wind energy, wind climate, etc., was prospected to be established to provide a systematic data support for power plant site selection, daily operation, long-term planning, environmental safety guarantee for resource development.

The short-term wind power forecasting is closely linked to the collection and transformation of resources and their applications such as unit scheduling and power trading. Previous researchers have done tremendous work on the short-term forecasting of offshore wind power across the globe. However, there are no such studies specifically targeting the countries and regions along the Maritime Silk Road. Besides, current forecasting products mainly focuses on parameters such as wind speed and density while key indicators like usability, ELO, and storage amount have not been covered. In this chapter, an all-element short-term forecasting system of offshore wind energy resource was designed, comprehensively including the wind field, wind power density, energy availability, energy level occurrences, energy storage, etc., as well as the forecasting of wind energy of key nodes (hourly wind power density and wind direction, wind energy rose, etc.), to provide reference for the daily operation of wind power generation and so on.

The offshore wind energy can provide strong power support to overcome the energy crisis, as well as good opportunity for international exchange and cooperation, an antidote to climate change and conventional energy shortage, and important support to achieve the carbon neutrality target. Evaluation of resources and planning should go first. However, such evaluations are still relatively few owing to the difficulties faced by marine observation and shortage of research resources, which has been a barrier to developing offshore wind power into a bigger scale as an industry. Firstly, this chapter discussed the research status and difficulties (detail research on the climatic characteristics of wind energy, macro and micro-scale energy classification, relationship between wind energy and key indexes, short-term forecast of wind energy, climatic variation of wind energy, mid-long term projection of wind energy, energy evaluation on key nodes). Then the countermeasures to deal with the difficulties are provided.

Sri Lanka is located near the center of the main channel of the Indian Ocean. It is also an important place for the Maritime Silk Road initiative. This chapter carried out a technology of offshore wind energy evaluation on key island or vital region, with the Sri Lankan waters as a case study. Systematically considering the wind power density, energy availability, energy level occurrences, energy direction (co-occurrence of wind power density-wind direction), climatic variation of wind energy, wind class, the wind energy of Sri Lankan waters for the future and for the past are analyzed and compared, to provide reference for the long-term planning of wind energy development and technical approach for the mid-long term projection of wind energy resource.

How to extract useful and key information from the original big wave data with large volume and low information density and establish a wave climate datasets is still in a blank status, which is also urgently needed in the ocean navigation, deep sea development, marine resource development, offshore construction, and disaster prevention and mitigation, etc. Based on the ERA-interim wave data for 1979–2018 and ERA-40 wave reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF), this study adopts the climate statistical analysis methods to establish the first open and public wave climate dataset for the Maritime Silk Road, systematically including the prevailing wave direction and significant wave height (SWH), wave period, swell index (SI), rough sea occurrence, wave direction occurrence, co-occurrence of wave height and wave direction, co-occurrence of wave height and wave period, and extreme wave height. It is hoped that the results can provide a data support for the safe and efficient deployment of marine development and construction.

With the advancement of sonar technology and inverse problem theory, the use of acoustic signals to retrieve the parameters of underwater acoustic environments has received more and more attention. Based on the parabolic equation (PE) solver and genetic algorithm, this paper studied the problem of jointly inverting the water sound speed profile (SSP) and geoacoustic parameters, where the dimension of water SSP was greatly reduced by the EOFs. Numerical experiments show that this method can provide a high accurately retrieved results with noise free case observations, as well as a favorable anti-noise ability for 3dB Gaussian noise case.

Macrophages participate in all stages of sepsis and affect immune homeostasis and inflammatory processes. Small ubiquitin-like modifier (SUMO) protease SENP1 plays an important role in cellular inflammation by regulating proteins in SUMOylation. However, the roles and related mechanisms of SENP1 in macrophage inflammation during sepsis are largely unknown. In the present study, SENP1 expression was significantly promoted in lipopolysaccharide (LPS)-induced RAW 264.7 cells; furthermore, the knock down of SENP1 reduced the expression of inflammatory cytokines interleukin-6 and tumor necrosis factor-α. Momordin Ic (MC), a new type of SENP1 inhibitor, reduces LPS-induced cellular inflammation by depressing SENP1 expression. Moreover, the effect of SENP1 on LPS-induced inflammatory response was dependent on SENP1-Sp3 interaction and the promotion of Sp3 expression via Sp3 deSUMOylation. Furthermore, MC-depressed Sp3 expression disturbed Sp3-nuclear factor (NF)-κB interaction and then alleviated LPS-induced cellular inflammation. These results suggest that SENP1 promotes LPS-induced macrophage inflammation by promoting Sp3 expression via deSUMOylation and Sp3-NF-κB interaction in sepsis.

During the resource crisis, swell energy received an increasing amount of attention due to its stability, huge energy storage and dominant role in the mixed wave. Investigation of the swell propagation is beneficial for wave energy forecasting, swell monitoring and warning and so on. However, little research has been conducted on this topic so far. The traditional method is to choose a region in advance (short as pre-chosen region) and then detect the propagation termination of the swell of this region, which is limited in effectiveness because the swell of the pre-chosen region may not propagate to the area focused. Based on the 40-year European Centre for Medium-Range Weather Forecast (ECMWF) re-analysis (ERA-40 wave reanalysis), this study proposed a back-stepping method to trace the source of swell energy. The Clipperton Island waters are selected as a case study. Results show that the back-stepping method is an effective way to trace back the source of swell energy. The swells of the Clipperton Island waters mainly come from the winter Hemisphere. The swells need about 120 hours to propagate from the Hawaii waters to the Clipperton Island waters, while 180 hours to propagate from the Maria-Theresa Reef to the Clipperton Island waters.

Wave energy development will help ease resource crises. The projection of wave energy has practical value for the long-term planning of energy development (implementation of power generation, trading strategies, and so on). This paper proposed a wave energy projection program. South China Sea (SCS) and the East China Sea (ECS) in 2019 were carried out as case studies using the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset to drive the WAVEWATCH-III (WW3) wave model. The multiyear average wave energy of the SCS and ECS was presented. A comparison of the projected values with multiyear averages of the wave energy could positively contribute to the planning of the wave energy development. The results show that the SCS possessed relatively rich energy for both the past and future and that January and October possessed the highest wave power density (WPD). The projected annual average WPD in 2019 was similar to the multiyear average WPD in the north and middle of the ECS, slightly higher than the multiyear average in the south of the ECS, and considerably greater than that in the SCS. The projected WPDs in January, April, and October 2019 were higher than the multiyear averages in the corresponding months. In July, the projected WPD in the SCS was smaller than the multiyear average, while the opposite was observed in the south of the ECS. The projected effective wave height occurrence (EWHO) and the occurrence of WPD >2 kW/m in 2019 were also superior to the multiyear average values.

The Equatorial Undercurrent (EUC) plays an important role in ocean circulation and global climate change. Near the equator, as the Coriolis parameter goes to 0, equatorial currents cannot be described by geostrophy in which the pressure gradient force term is balanced by the Coriolis force term. Many previous studies focus on the relationships between the EUC and El Nino-Southern Oscillation (ENSO), the thermocline, sea surface topography, the distribution of equatorial wind stress and other atmosphere-ocean factors. However, little attention has been paid to the northward pressure gradient (NGT), which may also be important to the EUC. The pressure can be regarded as a complex nonlinear function of terms including temperature, salinity and density. This study attempts to reveal the connection between a function of the northward pressure gradient (FNP) and the EUC. The connection is derived from primitive equations, by simplifying the equations with using scaling analysis, and shows that the beta effect may be the main reason why the FNP is important to the EUC. The vertical structure of the EUC can be partially described by the FNP. The NGT has an obvious influence on the EUC while the eastward pressure gradient has a relatively smaller effect.

Against a backdrop of increasing energy demand, the development of wave energy technology is a logical means of both meeting this demand and mitigating the environmental degradation associated with conventional power generation. Previous research has made considerable progress in the climatic characterization and short-term forecasting of wave energy. However, medium- to long-term predictions of wave energy resources, which are central to the development of future operating and trading strategies, remain scarce. This study provides an overview of long-term climatic trends and medium- to long-term predictions of wave energy, before discussing the focus of future predictions. Finally, a new method is proposed for predicting wave energy resources on a medium- to long-term basis that incorporates the swell index and propagation characteristics of swell energy. This model was developed with the aim of improving the precision of wave energy predictions, thereby providing a reference for the effective utilization of wave resources. The results of this study demonstrate that long-term climatic trend analysis should include not only variations in wave power density (WPD), but also long-term variability in wave energy stability, energy level occurrence, and variability in the occurrence of effective significant wave height (SWH). The medium- to long-term prediction of wave energy should also synthetically consider the above factors. We conclude that monitoring the propagation of swell energy and calculating the swell index constitutes a robust theoretical basis for predicting the WPD of mixed wave.

Wind and waves are key components of the climate system as they drive air-sea interactions and influence weather systems and atmospheric circulation. In marine environments, understanding surface wind and wave fields and their evolution over time is important for conducting safe and efficient human activities, such as navigation and engineering. This study considers long-term trends in the sea surface wind speed (WS) and significant wave height (SWH) in the China Seas over the period 1988–2011 using the Cross-Calibrated Multi-Platform (CCMP) ocean surface wind product and a 24-year hindcast wave dataset obtained from the WAVEWATCH-III (WW3) wave model forced with CCMP winds. The long-term trends in WS and SWH in the China Seas are analyzed over the past 24 years to provide a reference point from which to assess future climate change and offshore wind and wave energy resource development in the region. Results demonstrate that over the period 1988–2011 in the China Seas: 1) WS and SWH showed a significant increasing trend of 3.38 cm s−1 yr−1 and 1.52 cm yr−1, respectively; 2) there were notable regional differences in the long-term trends of WS and SWH; 3) areas with strong increasing trends were located mainly in the middle of the Tsushima Strait, the northern and southern areas of the Taiwan Strait, and in nearshore regions of the northern South China Sea; and 4) the long-term trend in WS was closely associated with El Nino and a significant increase in the occurrence of gale force winds in the region.

The propagation characteristics of swell energy are important for applications such as swell wave power generation, ocean wave forecasting, disaster prevention and reduction, etc. However, the study on the propagation of swell energy is still relatively rare. This study proposed a new method to exhibit the exact propagation route and speed and intraseasonal oscillation of swell energy. The Indian Ocean is selected as a case study, using the 45-year (1957.09–2002.08) European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-40) wave reanalysis dataset. The main results are as follows. (1) empirical orthogonal function (EOF) analysis and 6-hourly zonal averages of swell wave power density (WPD) show that the Indian Ocean swell energy has an obvious but fluctuating northeastward propagation. (2) The swell energy of the southwest Indian Ocean generally spreads to two regions: the main body propagates in a NE–NNE direction to Sri Lankan waters, while a small branch propagates to Christmas Island waters. (3) Swell energy in the west of the southern Indian Ocean westerly (SIOW), in Sri Lankan waters, and in Christmas Island waters share a common period of quasi-weekly oscillation. Swell energy takes ∼6 days to propagate from the west of the SIOW to Sri Lankan and Christmas Island waters. Swell energy propagates faster during June–September than in other months.

Through the work of this book, a series of core challenges in wave energy assessment of Maritime Silk Road have been solved, such as the climate background characteristics of the wave energy resources (spatial-temporal distribution of a series of key parameters of wave energy), the historical trend of each key parameters, the short-term forecast of wave energy, the long-term projection of wave energy, wave energy assessment and proposals for maritime key points, etc. Planning to build a wave energy resource dataset of the Maritime Silk Road, and the results can provide a scientific basis for the site selection, daily operation, and long-term planning of wave power generation, seawater desalination and other wave energy projects. In the future, it is necessary to strengthen study on the following aspects: wave energy classification, propagation characteristics of swell energy and swell monitoring and warning, risk assessment of marine environment, the construction of a comprehensive application platform for wave energy resources, to improve the support capability for wave energy development.

The application and sharing of scientific data has become an important indicator of the national science and technology level. With the rapid development of observation methods and numerical models, ocean data has also exploded, and “big data” has entered people’s sight. How to extract useful information of energy assessments from large ocean data with low information density, and establish the marine energy dataset is the key to rational and efficient development of wave energy. At present, the global marine energy dataset is rare, and the dataset of wave energy of the Maritime Silk Road is still in blank. Based on the previous research results, this chapter propose to build the wave energy resource dataset of Maritime Silk Road, which is close to actual demand, convenient to query and perfect in theoretical system. The data is also the first wave energy resource dataset of Maritime Silk Road at home and abroad. In the future, the program can be widely used in the construction of marine new energy datasets of offshore wind energy and ocean current energy, to provide data support for researchers and engineers actively participating in the marine new energy development of the Maritime Silk Road.

The maritime key points are important support for marching into the deep blue. They are usually based on the remote islands and reefs, and the power dilemma has always been a worldwide difficulty. Carrying out the wave power generation will help the islands and reefs to achieve electricity self-sufficiency. Evaluation always goes first, followed by resource development. Previous researchers have made great contributions to the study of wave energy in various sea areas around the world. But so far, the wave energy research on the maritime key points of the North Indian Ocean is almost blank. This chapter uses the ERA-interim wave data from the ECMWF to conduct a systematic analysis of the wave energy resources in the Sri Lankan waters (an important node of the Maritime Silk Road), comprehensively including the monthly WPD, monthly available rate, monthly energy level frequency, energy direction and sea state contribution in representative months, monthly energy stability, monthly exploitable storage and long-term trend of wave energy, to provide scientific and technological support and proposal for marching into the deep blue.

Current research on long-term predictions of meteorological and oceanic elements is abundant, but research on long-term prediction of wave energy is scarce, which is urgently needed in the long-term planning of resource development. The long-term projection of wave energy can be usually achieved by the following three methods: (1) Analyzing the relationship among wave energy, MJO (Madden-Julian Ocillation), nino3 and other important factors, combined with the long-term trend characteristics of wave energy, to achieve medium- and long-term prediction of wave energy; (2) with wave model driven by CMIP (Coupled Model Intercomparison Project) data, to project the future wave energy; (3) using methods of Hilbert Huang, artificial neural network (ANN), etc., combined with long-term sequence wave energy data to make long-term projection of wave energy. This chapter proposed a long-term projection scheme of wave energy, with the CMIP5 data to drive the current international advanced numerical wave model WW3, to simulate the global wave field from 2020 to 2059, and then project and analyze the wave energy of the Maritime Silk Road for the future 40 years. The projection parameters include the WPD, available rate, energy richness, stability, and monthly variation, to provide scientific evidence for long-term planning of resource development.

Lots of research on the long-term trends of meteorological and oceanic factors has been conducted. However, the research on the climatic trend of wave energy is extremely scarce, which is closely related to the medium- and long-term planning of resource development and predictive ability of wave energy. And the traditional analysis on long-term trends of resources was mainly concerned with the trend of WPD. In the actual wave energy development, energy stability (Cv, Mv, Sv) can affect acquisition and conversion efficiency, as well as equipment life. The EWHO reflects the available rate of wave energy, and the energy level frequencies (ALO, MLO, RLO) reflects the energy enrichment. This chapter analyzed the climatic trends of a series of key parameters of wave energy along the Maritime Silk Road for the first time at home and abroad, comprehensively including the trends of WPD, Cv, Mv, Sv, EWHO, ALO, MLO, RLO, etc. The analysis content of climatic trend includes the overall trend, seasonal difference and regional difference of the trend, dominant season and region of the trend, to lay a theoretical foundation for improving the medium- and long-term planning ability of wave energy development and predictive ability of wave energy.

Previous researchers have made great contribution to the analysis of the wave energy climatic characteristics of many sea areas globally. But there are a few researches about the Maritime Silk Road. In the systematic aspect of the study of wave energy climatic characteristics, the elements of analysis are still not comprehensive enough. Besides considering traditional wave power density (WPD), energy stability, energy storage, it is necessary to take full consideration of the available rate, energy richness, energy direction, and the contribution of different sea states to wave energy, which are also closely related to the development of the wave energy. Scarce materials, large reserves and huge amount calculations and the high technical requirements have greatly increased the difficulty of systematic research on wave energy. For the first time, this chapter presented the spatio-temporal distribution characteristics of a series of key parameters of wave energy of the Maritime Silk Road by utilizing ERA-interim wave data from the ECMWF, to provide technical support (site selection, daily operation, etc.) for the development of resource such as wave power generation and seawater desalination.