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Renewable energy known as green energy production had been rapidly developed to replace fossil fuel generators. Solar energy is one of the most utilized renewable energy because it can be obtained almost everywhere on earth. Solar energy is an unlimited energy source and it can be harvest in the safest way compared to other renewable energy source. Since the development of the photovoltaic system requires a lot of investment, preliminary testing had been carried out to uncover the solar energy potential based on the specific locations including the development of a solar energy potential measuring system. This paper presents the development of self-operating solar energy potential measuring system by using an ATmega 328P microcontroller-based setup. The system not only can measure the solar energy potential but using solar energy generates with its solar panel to operates the whole system. Based on the results, the system developed had successfully operated and measures the solar energy value at three different locations under different weather conditions.

The radiant energy from the sun is defined as solar radiation. It had been discovered as a renewable energy which can provide electricity supplies using a photovoltaic system. Before developing the system, a preliminary test must be carried out to perform the analysis of solar energy potential in that specific area. This preliminary test is known as a modeling technique. The technique will use the related parameters as an input to predict the solar radiation value. Since there are multiple parameters used for solar radiation prediction model development, there had been multiple attempts on using only certain parameters to produce predictions for solar radiation value. This paper will review and further analyzed several works presented by the previous studies on developing solar radiation prediction models using various parameters with their results. With the findings, the implementation of the Neural Network Autoregressive Model with Exogenous Input (NNARX) on solar radiation prediction carried out for the different input parameter configurations. Based on the results, it shows that the solar radiation prediction model development using more input parameters produced the best prediction performance with the R2 value of 0.9329.

Renewable energy resources such as photovoltaic (PV) are crucial to counter an incoming energy crisis in the future. Nevertheless, when PV generators are integrated with storage batteries, a constructive mechanism needs to be structured in order to securely control the energy flow in the batteries during the charging/discharging process so that the risk of over-charge/over-discharge of the batteries can be prevented. Furthermore, it is extremely essential to implement a control method that is capable to fully utilize a stored energy in the scope of small-scale BESS to the load regularly in the first place before it is further scaled up to a mega-structure. In this study, an energy control scheme that considers and executes a next-day forecast of generation based on Grid Point Value as an input data has been proposed. Experimental equipment was structured and the system’s operation was completely administered by an RX621 microcontroller. Good experimental results were obtained corresponding to the trend of simulation results.