Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

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

• Author(s):
• DOI:

$16.00 (plus tax if applicable) ##### Buy Knowledge Pack 10 chapters for$120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:

Intelligent Wireless Communications — Recommend this title to your library

## Thank you

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
• 15.3.4.1 Cross-validation
• 15.4 Neural networks
• 15.4.1 Different types of neural networks
• 15.4.2 Time series forecasting with neural network
• 15.4.2.1 Data preparation
• 15.4.2.2 Modeling
• 15.4.2.3 Forecast
• 15.4.2.4 Evaluate results
• 15.5 Compare methods
• References

Preview this chapter:

Forecast of electricity consumption: a comparison of ARIMA and neural networks, Page 1 of 2

| /docserver/preview/fulltext/books/te/pbte094e/PBTE094E_ch15-1.gif /docserver/preview/fulltext/books/te/pbte094e/PBTE094E_ch15-2.gif

### Related content

content/books/10.1049/pbte094e_ch15
pub_keyword,iet_inspecKeyword,pub_concept
6
6
This is a required field