Net Metering Prediction in Prosumer Building With Temporal Fusion Transformer Models

Abstract

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.

Keywords

• Deep learning
• sustainable development goals
• transformer model
• prosumer building
• energy forecasting