Abstract
The goal of this paper is to predict electrical energy consumption using nonlinear autoregressive (NAR) models. The practical section contains historical data on Iraq’s annual electricity consumption rate from 1980 to 2013. The most significant findings are that neural networks perform better at predictive analytics due to the hidden layers. To make predictions, linear regression models only use input and output nodes. The hidden layer is also used by the neural network to improve prediction accuracy. This is because it ‘learns’ in the same way that humans do. It is recommended that further research be undertaken in the following areas Intelligent forecasting methods are being used as an alternative to traditional forecasting methods.
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Ashour, M.A.H., Alashari, O.M.N. (2022). Electricity Consumption Forecasting in Iraq with Artificial Neural Network. In: Ahram, T., Taiar, R. (eds) Human Interaction, Emerging Technologies and Future Systems V. IHIET 2021. Lecture Notes in Networks and Systems, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-030-85540-6_117
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DOI: https://doi.org/10.1007/978-3-030-85540-6_117
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