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Forecast of electricity consumption: a comparison of ARIMA and neural networks

Forecast of electricity consumption: a comparison of ARIMA and neural networks

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Electricity consumption is a critical factor in the climate change problem. The in time and reliable prediction of future consumption can help experts take the appropriate measures to eliminate electricity production side effects on the planet. Experts also can use forecasts to design suitable renewable energy systems. In this chapter, we analyze two well-known forecasting models. The first is the autoregressive integrated moving average (ARIMA), which has been used in many real-life cases in the previous years, and the second one is the neural network forecasting method which, is based on human's brain function. Each method is analyzed with its implementation and steps. The last section is a head-to-head comparison of the two methods.

Chapter Contents:

  • 15.1 Introduction
  • 15.2 Data overview
  • 15.2.1 Import data
  • 15.2.2 Data information
  • 15.3 ARIMA
  • 15.3.1 Data preparation
  • 15.3.2 Modeling
  • 15.3.3 Forecast
  • 15.3.4 Evaluate results
  • Cross-validation
  • 15.4 Neural networks
  • 15.4.1 Different types of neural networks
  • 15.4.2 Time series forecasting with neural network
  • Data preparation
  • Modeling
  • Forecast
  • Evaluate results
  • 15.5 Compare methods
  • References

Inspec keywords: neural nets; power consumption; autoregressive moving average processes; power generation planning; climate mitigation; renewable energy sources; power engineering computing

Other keywords: neural network forecasting method; electricity consumption; ARIMA; climate change problem; electricity production side effects; autoregressive integrated moving average; renewable energy systems

Subjects: Other topics in statistics; Probability theory, stochastic processes, and statistics; Other topics in statistics; Power system planning and layout; Energy resources; Power engineering computing; Energy utilisation

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