An Adaptive Backpropagation Algorithm for Long Term Electricity Load Forecasting

Nooria Mohammed, Ammar Al Bazi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Artificial Neural Networks (ANNs) have been widely used to determine future demand for power in the short, medium, and long terms. However, research has identified that ANNs could cause inaccurate predictions of load when used for long-term forecasting. This inaccuracy is attributed to insufficient training data and increased accumulated errors, especially in long-term estimations. This study develops an improved ANN model with an Adaptive Backpropagation Algorithm (ABPA) for best practice in the forecasting long-term load demand of electricity. The ABPA includes proposing new forecasting formulations that adjust/adapt forecast values, so it takes into consideration the deviation between trained and future input datasets' different behaviours. The architecture of the Multi-Layer Perceptron (MLP) model, along with its traditional Backpropagation Algorithm (BPA), is used as a baseline for the proposed development. The forecasting formula is further improved by introducing adjustment factors to smooth out behavioural differences between the trained and new/future datasets. A computational study based on actual monthly electricity consumption inputs from 2011 to 2020, provided by the Iraqi Ministry of Electricity, is conducted to verify the proposed adaptive algorithm's performance. Different types of energy consumption and the electricity cut period (unsatisfied demand) factor are also considered in this study as vital factors. The developed ANN model, including its proposed ABPA, is then compared with traditional and popular prediction techniques such as regression and other advanced machine learning approaches, including Recurrent Neural Networks (RNNs), to justify its superiority amongst them. The results reveal that the most accurate long-term forecasts with the minimum Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) values of (1.195.650) and (0.045), respectively, are successfully achieved by applying the proposed ABPA. It can be concluded that the proposed ABPA, including the adjustment factor, enables traditional ANN techniques to be efficiently used for long-term forecasting of electricity load demand.

Original languageEnglish
Pages (from-to)(In-Press)
JournalNeural Computing and Applications
Volume(In-Press)
Early online date11 Aug 2021
DOIs
Publication statusE-pub ahead of print - 11 Aug 2021

Keywords

  • Adaptive backpropagation
  • Linear regression
  • Load demand
  • Long-term forecasting
  • MLP neural networks
  • Radial basis function networks
  • Recurrent neural networks

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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