Journal of Renewable Energy and Smart Grid Technology

Forecasting Energy Consumption from EV Station Charging Using RNN, LSTM and GRU Neural Network

Nivadee Klungsida — 2024-04-19

The increase in electric vehicles (EVs) has resulted in a substantial escalation in electricity consumption. This increased demand puts more stress on the overall power system. The current study offers a method to predict energy usage patterns by looking closely at when electric vehicles typically need to charge during the day. After that, the collected data were used to create a predictive model using three different deep learning methods: Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). This study employs data pertaining to electric power consumption for EVs charging derived from Kamphaeng Phet Rajabhat University. The practical results show that the proposed model significantly outperforms in predicting power needs at the mentioned charging spots. This is evident in its precise prediction of the total power demands using the algorithm. Among the three types of deep learning structures studied, it's clear that the LSTMs type stands out as the best, achieving the most accurate results. This is supported by a Root Mean Square Error (RMSE) of 0.372 and a Mean Absolute Percentage Error (MAPE) of 11.508%. Additionally, the inquiry facilitates a comprehensive comparison between the dynamics of demand and the parameters of supply. This process yields data that offers valuable insights crucial for the strategic identification of potential electric vehicle charging stations. It also enables the prudent utilization of remaining electrical capacity derived from production processes. These combined efforts converge to ensure the utmost extraction of utility.

Recognition of Multiple Power Quality Disturbances Based on Discrete Wavelet Transform and Improved Long Short-Term Memory Networks

Supakan Janthong — 2024-06-19

In the past few years, distributed electricity generation from renewable sources, or microgrid systems, has been connected to the grid to increase power supply stability. This responds to government policy regarding commitment to using 100% renewable energy in operations (RE100) project efforts. This results in the entry of power electronic or non-linear equipment into the electrical system, making it more sensitive. Moreover, multiple power quality disturbances (PQDs) consist of a variety of single disturbances. Analysis of complex multi-label patterns is a challenging task. In this paper, we propose a methodology to address this challenge by leveraging Discrete Wavelet Transform (DWT) and Improved Long Short-Term Memory Networks (LSTM). Firstly, multiple PQDs are synthesized utilizing a mathematical model based on IEEE standards 1159-2019. Secondly, the obtained PQDs are decomposed into nine feature classes, yielding detailed (cDs) and approximation (cAs) coefficients through Five-Level DWT Decomposition. Furthermore, we conducted a comparative analysis of each component across five different wavelet functions: haar1, db4, bior1.3, coif2, and sym4. Thirdly, the cDs and cAs coefficients derived from each wavelet type undergo statistical analysis before being inputted into the LSTM model for classification of each feature class. Our results highlight that cD5 components obtained from the db4 wavelet exhibit the highest accuracy rate of 93.86%. This finding elucidates the significance of selecting appropriate wavelet types and compositions for the successful classification of multiple PQDs.

Evaluation of the Solar Absorption and Economics of Steel Roofs for Heat Sources

Janejira Yoochareon — 2024-06-20

Energy conservation in buildings and homes helps reduce energy use and save on energy costs. This study was to examine how the absorption of solar radiation (α) affects the amount of cooling load of colors steel roof. That impacts the energy costs of air conditioning compared to a tiled roof. The study used a spectrophotometer to analyze solar radiation’s heat-absorbing properties in the 300-2,500 nm wavelength range. The roof uses a steel area of 1,000 m² with 10 metal roof coating colors (red, brown, gray, bluish-green, light-green, dark-green, orange, light-blue, dark-blue, and colorless). The result was found that gray, brown, dark-green, dark-blue, light-green, bluish-green, red, light-blue, orange and colorless metal roofs absorbed varying levels of heat from sunlight. Interestingly, colorless metal roofs had the least impact on heat absorption, resulting in the maximum energy saving effect in the air conditioning system, amounting to 125,085.32 baht per year. Furthermore, the break-even value was calculated to be 0.21 years.