Evaluation of Dimensionality Reduction Techniques for Load Profiling Application in Smart Grid Environment

The traditional power grid system is evolving toward a smart grid system, which will improve the user experience. However, this system is capable of generating high-dimensional data at a very high sample rate. A common technique for reducing high-dimensional data is dimensionality reduction. With this technique, we are able to reduce the data to a lower dimension, making it suitable for smart grid applications including transmission, storage, and visualization. Linear dimensionality reduction techniques are mostly explored in the context of smart grid applications. Due to the nonlinear nature of data generation in the smart grid, we anticipate that the nonlinear dimensionality reduction techniques can perform better. This work evaluates different nonlinear dimensionality reduction techniques and compares them with principal component analysis, which is a widely used linear dimensionality reduction technique in the smart grid environment. We use the visualization of load profile data and adjusted rank index (ARI) for comparison of dimensionality reduction techniques. Load profiling is an important task to complement the demand-side management and tariff selection. The visual depiction of the load profiles and ARI suggest that the nonlinear dimensionality reduction techniques perform better compared with linear dimensionality reduction techniques.