Time-Series Forecasting Models for Smart Meters Data: An Empirical Comparison and Analysis.

Accurate energy consumption forecasting is essential in the decision-making process, and in optimizing energy production and distribution to meet customers' demands, especially given the fluctuating demand. The widespread deployment of smart meters has revolutionized the collection of time-series data on energy consumption, providing detailed insights into usage patterns at a granular level. This paper presents a comprehensive comparison of eight time-series forecasting models: Autoregressive Integrated Moving Average (ARIMA) and Seasonal ARIMA (SARIMA), Decision Trees (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Long Short-TermMemory networks (LSTM) to assess the most efficient model on the smart meters' dataset. The models are evaluated using several statistical metrics, and based on the experimental results analysis, the LSTM model provided the best prediction performance with an RMSE of 2.106, a MAPE of 0.19, and a MAE of 1.599. [ABSTRACT FROM AUTHOR]