Electricity demand load forecasting of the Hellenic power system using an ARMA model

Pappas, Stylianos Sp.; Ekonomou, Lambros; Karampelas, Panagiotis; Karamousantas, D.C.; Katsikas, Sokratis K.; Chatzarakis, George E.; Skafidas, P.D.

doi:10.1016/j.epsr.2009.09.006

Cited by 155 publications

(55 citation statements)

References 28 publications

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“…As for the statistical models, the auto-regressive moving average (ARMA), auto-regressive integrated moving average (ARIMA), generalized autoregressive conditional heteroskedasticity (GARCH), vector auto-regression (VAR), and Kalman filters methods are widely used in the electric load forecasting areas. For example, Pappas et al [7] established an ARMA model for short-term electric load forecasting, and the results showed the good performance of the proposed model. Kavousi-Fard and Kavousi-Fard [8] proposed a new hybrid model based on ARIMA for short-term load forecasting.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

et al. 2017

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Abstract:The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting.

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Section: Introductionmentioning

confidence: 99%

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

et al. 2017

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“…In particular, it has been shown that an autoregressive-moving-average (ARMA) model is an effective tool to predict wind generation [7], [8] as well as electricity demand [9], [10], [11]. Moreover, unlike the Markov-chain model [5], the ARMA-based prediction models do not require discretization and can potentially capture correlation between distant time steps.…”

Section: Introductionmentioning

confidence: 99%

Risk-limiting power grid control with an ARMA-based prediction model

Ono

Topcu

et al. 2013

52nd IEEE Conference on Decision and Control

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Abstract-This paper is concerned with the risk-limiting operation of electric power grids with stochastic uncertainties due to, for example, demand and integration of renewable generation. The main contribution is incorporating autoregressive-moving-average (ARMA) type prediction models for the underlying uncertainties into chance-constrained, finitehorizon optimal control. This uncertainty model leads to a more (compared to existing work in literature) careful treatment of correlation in time which is significant especially in renewable generation yet has attracted limited attention. The paper first discusses how the resulting chance-constrained optimization problems can be solved computationally and demonstrates the effects of the use of the proposed prediction models through simulation-based case studies with realistic data.

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“…References [36][37][38][39][40] refer to application of hybrid wavelet and ANN [36], or wavelet and Kalman filter [37] models to short-term load forecasting STLF, and a Kalman filter with a moving window weather and load model, for load forecasting is presented by Al-Hamadi et al [38]. In the Greek electricity market Pappas et al [39] applied an ARMA model to forecast electricity demand, while an ARIMA combined with a lifting scheme for STLF was used in [40].…”

Section: Introductionmentioning

confidence: 99%

“…In the Greek electricity market Pappas et al [39] applied an ARMA model to forecast electricity demand, while an ARIMA combined with a lifting scheme for STLF was used in [40].…”

Section: Introductionmentioning

confidence: 99%

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

et al. 2016

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Abstract:In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

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Electricity demand load forecasting of the Hellenic power system using an ARMA model

Cited by 155 publications

References 28 publications

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting

Risk-limiting power grid control with an ARMA-based prediction model

Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoothing) and Artificial Intelligence Models (ANN, SVM): The Case of Greek Electricity Market

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