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