Updating of Daily Load Prediction in Power Systems Using Ar-Models

Fischer, Jiří; Wilfert, H.-H.

doi:10.1016/b978-0-08-033452-3.50048-x

Cited by 1 publication

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“…In statistical models, a potential dynamic relationship between current information and historical data is deemed to exist, and this relationship is described using mathematical statistics methods under strict assumptions. Models of this category, such as the Auto Regressive (AR) model [4], the Auto Regressive Moving Average (ARMA) model [5], the Auto Regressive Integrated Moving Average (ARIMA) model [6], and the Seasonal Model (SM) [7], have been applied to electricity load forecasting for many years. In 2011, Li et al [8] proposed an improved Grey Model (GM) for use in short-term load forecasting.…”

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confidence: 99%

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Wang

Tang

et al. 2019

Energies

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Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

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