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The government has set an ambitious target of renewable energy development in Malaysia. As a promising renewable energy source, wind energy plays an important role in the Malaysia renewable energy roadmap. Compared to onshore wind energy, offshore wind resources with better quality can be provided in the areas away from the coast, which has greater potential to contribute to electricity generation. Wind and bathymetry conditions are two important factors that determine the feasibility and economics of offshore wind turbines. In this paper, an investigation is conducted on wind and bathymetry conditions around Malaysia. The data source mainly originates from the Global Wind Atlas. The conditions of the coastal areas in different states and federal territories of both Peninsular Malaysia and East Malaysia are analysed, with a specific focus on wind speed, wind energy density, and bathymetry conditions in high-wind-speed regions. The data and survey are verified and compared with the past published literature. This paper aims to investigate the wind and bathymetry conditions around Malaysia, assess the potential of offshore wind energy, discuss the feasibility of offshore wind turbines, and provide references for offshore wind development in Malaysia. [ABSTRACT FROM AUTHOR]

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg–Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models. [ABSTRACT FROM AUTHOR]

Advancements in space technology have enabled the acquisition of reliable marine data, facilitating research on the potential of ocean renewable energy as an alternative source that can reduce dependency on fossil fuels, subsequently mitigating climate change. However, the ocean renewable energy development in Malaysia has not received adequate attention from local authorities and communities due to low resources in this area. Insufficient in-situ data, both in spatial and temporal dimensions, poses challenges in investigating the characteristics of ocean parameters, hindering a thorough study of the potential for ocean renewable energy development. Hence, this paper aims to identify potential ocean renewable energy development locations using the altimetry data and spatial-temporal clustering using the K-means technique. Theoretically, Malaysian seas are suitable for harnessing wind and waves with energy density ranges of up to 104.69 kW/m2 and 4.21 kW/m, respectively. This study enhances the understanding of Malaysian potential for ocean renewable energy, providing valuable information to stakeholders and the government to increase their interest in ocean renewable energy as a sustainable source for electricity generation in the future. [ABSTRACT FROM AUTHOR]