Solar energy is currently an underutilized renewable energy source that could fulfill low-temperature industrial heat demands with significant potential in high solar irradiance counties such as Malaysia. This study proposes a new systematic method for optimization of solar heat integration for different process options to minimize the levelized cost of heat by combining different methods from the literature. A case study from the literature is presented to demonstrate the proposed method combined with meteorological data in Malaysia. The method estimates capital cost and levelized cost of solar heating considering important physical constraints (e.g., available space) and recovery of waste heat. The method determines and optimizes important physical dimensions, including collector area, storage size, and control design. As the result of the case study, the solar thermal integration with Clean-In-Place streams (hot water) gives the lowest levelized cost of heat with RM 0.63/kWh (0.13 EUR/kWh) due to its lowest process temperature requirement. The sensitivity analysis indicates that collector price and collector efficiency are the critical parameters of solar thermal integration.
Today, solar energy is used in a many different ways. One of the most popular technological developments for this purpose is photovoltaic conversion to electricity. However, power fluctuations due to the variability of solar energy are one of the challenges faced by the implementation of photovoltaic systems. To overcome this problem, forecasting solar radiation data several minutes in advance is needed. In this research, a methodology to forecast solar radiation using cloud velocity and cloud moving angle is proposed. Generally, a red-toblue ratio (RBR) color model and correlation analysis are used for obtaining the cloud velocity and moving angle. Artificial neural network (ANN) forecast models with different input combinations are established. This methodology requires lower computational time since it only uses part of the pixels in the sky image. Based on R-squared analysis, it can be concluded that the ANN model with inputs of cloud velocity and moving angle and average solar radiation showed the highest accuracy among other combinations of inputs. The R-squared value was 0.59 with only a relatively small sample size of 42. The proposed model showed a highest improvement of 75.79% when compared to the ANN model based on historical solar radiation data only.
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