Industrial Engineering in the Big Data Era pp 259-270 | Cite as

# Short Term Electricity Load Forecasting with a Nonlinear Autoregressive Neural Network with Exogenous Variables (NarxNet)

## Abstract

Electricity load forecasting and planning have vital importance for suppliers as well as other stakeholders in the industry. Forecasting and planning are relevant issues that they provide feedback to each other to increase the efficiency of management. Accurate predictions lead to more efficient planning. Many methods are used for electricity load forecasting depending on characteristics of the system such as stationariness, non-linearity, and heteroscedasticity of data. On the other hand, in electricity load forecasting, forecasting horizons are important issues for modeling time series. In general, forecasting horizons are classified into 3 categories; long-term, mid-term and short-term load forecasting. In this paper, we dealt with short-term electricity load forecasting for Istanbul, Turkey. We utilized one of the efficient nonlinear dynamic system identification tools to make one-step ahead prediction of hourly electricity loads in Istanbul. In the final, the obtained results were discussed.

## Keywords

Prediction NarxNet Short-term electricity load forecasting## References

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