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Cluster Analysis and Model Comparison Using Smart Meter Data.

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Summary
This summary is machine-generated.

Accurate short-term load forecasting using time-series models like ARIMA is vital for smart grids. The study found ARIMA (1,1,1) offered the highest accuracy for predicting electricity consumption.

Keywords:
ARIMASGSCartificial neural networkregressionsmart gridsmart meter

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Area of Science:

  • Electrical Engineering
  • Data Science
  • Time Series Analysis

Background:

  • Load forecasting is essential for smart grid operations, influencing demand response, asset management, and investment decisions.
  • Accurate predictions are key for efficient grid management and resource allocation.

Purpose of the Study:

  • To explore the benefits of short-term load forecasting using various statistical and mathematical models.
  • To address computational challenges in time-series data analysis for load prediction.
  • To present a business case for analyzing customer consumption patterns and predicting behavior.

Main Methods:

  • Utilized time-series forecasting techniques, including artificial neural networks, auto-regression, and Auto-Regressive Integrated Moving Average (ARIMA) models.
  • Developed a business case to cluster data and identify factors influencing load consumption.
  • Evaluated model performance based on prediction accuracy.

Main Results:

  • The Auto-Regressive Integrated Moving Average (ARIMA) model with parameters (P, D, Q) set to (1, 1, 1) demonstrated the highest prediction accuracy.
  • Analysis of customer behavior based on consumption parameters was performed.

Conclusions:

  • Short-term load forecasting is critical for smart grid efficiency and planning.
  • The ARIMA (1,1,1) model is a highly accurate method for short-term load prediction.
  • Understanding customer consumption patterns enhances forecasting capabilities.