Cluster Analysis and Model Comparison Using Smart Meter Data

Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.

Automated summary

Load forecasting is crucial in the realm of smart grids, and this paper proposes time-series forecasting for short-term load prediction using statistical and mathematical models. A business case is presented to analyze different clusters and predict customer behavior, with the most accurate prediction model observed to be the ARIMA model with (P, D, Q) values of (1, 1, 1).