In this paper, we present a model for calculating the State of Health (SOH) of battery energy storage systems (BESSs) and battery capacity percentage, specifically tailored for grid-scale applications in Thailand. Unlike conventional models that rely on controlled laboratory data, the proposed approach uses actual operating temperature data for both development and validation, enabling a more correct assessment of battery performance under the high-temperature conditions typical of tropical climates. A set of coefficients derived from real operating data was incorporated, and the SOH results deviated by only 0.05% from theoretical values, proving high predictive accuracy beyond laboratory settings. Our findings revealed that capacity degradation rates in Thailand are approximately 20–60% higher than under the best conditions. Over a 10-year warranty period, battery capacity declined to approximately 80% at the lowest temperature range, 60% at the average range, and 40% at the highest range. By calculating both SOH and remaining capacity, the model provides a practical tool for lifespan prediction and system planning. Based on these findings, it is recommended that thermal management systems support battery operating temperatures between 25 and 35 °C and limit cooling losses below 10%, thereby mitigating energy yield degradation and ensuring efficient BESS operation. These results highlight the importance of incorporating real environmental data into degradation modeling. Future studies should include long-term monitoring of operating temperatures and cooling demand, economic analyses to enhance operational efficiency, and evaluation of external heat loads, particularly from solar radiation, to further refine predictions for tropical climates.

The growing global demand for electrical energy, together with the significant problem of increasing carbon dioxide emissions, have become urgent issues. The inefficient use of electricity exacerbates these challenges. Smart grid technology is emerging as a solution, employing innovative approaches to solve these problems. Within this context, this research emphasizes the identification of the most effective forecasting model for demand prediction. Utilizing the Transformer-based model, Temporal Fusion Transformer (TFT), together with the Naresuan University, School of Renewable Energy and Smart Grid Technology (SGtech) net metering dataset, we explored the influence of additional features on forecasting models, categorizing them into net metering data, weather-related attributes including temperature, dew point, weather conditions, and wind direction, and supplementary features related to the operational behavior of SGtech, specifically workday and time-of-day. Our experimentation shows that integrating workday and time-of-day data alongside net metering data significantly enhances prediction precision compared to other combinations. The TFT model outperforms popular time series forecasting models, including Neural Basis Expansion Analysis for Time Series (N-BEATS) and Neural Hierarchical Interpolation for Time Series (N-HiTS), in accuracy and parameter efficiency while maintaining inference times.

Smart power forecasting enables energy conservation and resource planning. Power estimation through previous utility bills is being replaced with machine intelligence. In this paper, a neural network architecture for demand side power consumption forecasting, called SGtechNet, is proposed. The forecast model applies ConvLSTM-encoder-decoder algorithm designed to enhance the quality of spatial encodings in the input feature to make a 7-day forecast. A weighted average ensemble approach was used, where multiple models were trained but only allow each model’s contribution to the prediction to be weighted proportionally to their level of trust and estimated performance. This model is most suitable for low-powered devices with low processing and storage capabilities like smartphones, tablets and iPads. The power consumption comparison between a manually operated home and a smart home was investigated and the model’s performance was tested on a time-domain household power consumption dataset and further validated using a real time load profile collated from the School of Renewable Energy and Smart Grid Technology, Naresuan University Smart Office. An improved root mean square error (RMSE) of 358 kwh was achieved when validated with holdout validation data from the automated office. Overall performance error, forecast and computational time showed a significant improvement over published research efforts identified in a literature review.