2013
DOI: 10.1007/978-3-319-01273-5_55
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Comparative Study of Grey Forecasting Model and ARMA Model on Beijing Electricity Consumption Forecasting

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“…Generally, the prediction approaches for energy consumption can be divided into two main categories, i.e., traditional models and artificial intelligence (AI) techniques. For traditional models, linear regression (LinR) [10] , gray system model (GM) [11,12] , autoregressive integrated moving average (ARIMA) [13,14] and error correction model (ECM) [14] have been widely applied to energy consumption forecasting. However, these traditional statistical models are usually performed under the data assumptions of linearity and stationarity, which may find difficulty in capturing nonlinear patterns hidden in hydropower consumption.…”
Section: Introductionmentioning
confidence: 99%
“…Generally, the prediction approaches for energy consumption can be divided into two main categories, i.e., traditional models and artificial intelligence (AI) techniques. For traditional models, linear regression (LinR) [10] , gray system model (GM) [11,12] , autoregressive integrated moving average (ARIMA) [13,14] and error correction model (ECM) [14] have been widely applied to energy consumption forecasting. However, these traditional statistical models are usually performed under the data assumptions of linearity and stationarity, which may find difficulty in capturing nonlinear patterns hidden in hydropower consumption.…”
Section: Introductionmentioning
confidence: 99%