PV systems have been increasingly installed worldwide in recent years. Because it produces clean energy, moreover the development of technology is continued therefore the reliability is increasing and the price is decreasing in opposite. To implement the PV system, however, a significant limitation of PV system is the uncertainty of power from the sun. This will affect the quality of the electrical system that connected. Therefore, this article will present the power forecasting of a PV system by calculating the solar radiation, collecting data from weather forecasting, and using Elman neural network to forecast by using data from PV system installed at roof top of Faculty Science and Technology Rajamangala University of Technology Thanyaburi.The results of study found that the tendency to apply this method any furthe

Remote areas that are not within the maximum breakeven grid extension distance limit will not be economical or feasible for grid connections to provide electrical power to the community (remote area). An integrated autonomous sustainable energy system is a feasible option. We worked on a novel multi optimization electrical energy assessment/power management system of a microgrid network that adopted combined dispatch, load-following, and cycle-charging strategies (control system) that acted as a power interface module over the hybrid configuration of energy sources (grid network/downdraft biomass generator/solar photovoltaic), thermal load controller-boiler systems, and hybrid energy-storage technologies (lithium, iron flow, sodium sulfur, and flywheel) to enable the microgrid network to operate in the island (off grid), grid, and island-able (ability to isolate itself when it is connected to the grid network) modes efficiently and effectively. An optimal multitask control algorithm and the storage units of modeled power generation sources were executed with the HOMER software application to improve the energy system’s efficiency, promote effective storage management, minimize energy loss, and improve the lifespan of the microgrid network. The integrated energy system can work for both rural and urban areas.

A Nonintrusive load monitoring (NILM) system is an energy demand monitoring and load identification system that only uses one instrument installed at main power distribution board. In this paper authors have used low sampling rate of monitored data to detect any change of power signal that obtained a 1 Hz sampling rate of active power from energy meter. Using Artificial Neural Network (ANN) for training steady-state real power and reactive power signatures. This paper point to four appliances including air conditioner television refrigerator and rice cooker. The results showed that in simulation test can disaggregation of appliances in correct detection rate 98% and in the installation test can disaggregation of appliances in correct detection rate 95%.

The power quality and energy yield of a large scale PV rooftop power plant in Samut Songkhram province are analyzed and presented in this paper. The power quality is examined and analyzed from the measured data to comply with the Provincial Electricity Authority (PEA) standard in Thailand. The measured parameters used in this study are as follows: the RMS Voltage, Frequency, Total Voltage Harmonic Distortion (THDv), and Voltage ripple. Certain parameters of measured data are used to calculate the distributed power yield and then compared with the Homer program simulation respectively. The investigated PV rooftop system has the installed capacity of 987.84 kWp. From the monitoring results, it found that the highest power yield was 778.125 kW while the simulation result was 783 kW. Moreover, based on the PEA standard EN 50160 with the cumulative percentile at 95% for PV rooftop power plant, the measured data showed that the power quality of this power plant passed the PEA regulations for its distribution network connecting system

Due to the fact that Photovoltaic (PV) systems must be practically installed in outdoor areas for converting solar radiation into electric power. For this reason, PV systems have to face the risk of damage from lightning. Mostly, PV Plants installed in Thailand, they have still not been installed lightning rods appropriately and engineers do not study lightning protection properly for PV systems. Then this paper has been gathering information about lightning protection in PV systems to develop a computer program for making a decision on either need to install lightning protection in PV system or not., the computer program can give a design on how to install lightning rods by using the protective angle method. This proposed computer program is tested by using the information from an existing 25kWp PV system installed at Thayang district, Phetburi province of Thailand. It was found that the existing 25k Wp PV system there faces a risk of damage from lightning strike on average 4.15 times per year and damage costs could be high. Accordingly, the proposed computer program can correctly design for lightning rod installation and verify the results by another 3D drawing program with the same method. From the results showed that all components of an existing PV system mentioned are properly protected by lightning rod. Consequently, the project owner is recommended that the existing PV system should be protected by installation of lightning rod