Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Pappas, Stylianos Sp.; Ekonomou, Lambros; Karamousantas, D.C.; Chatzarakis, George E.; Katsikas, Sokratis K.; Liatsis, Panos

doi:10.1016/j.energy.2008.05.008

Cited by 267 publications

(108 citation statements)

References 36 publications

(47 reference statements)

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“…Furthermore, neural network analysis requires a large data sample. In contrast, time-series analysis, such as the autoregressive and integrated moving average model (ARIMA), has become popular in electricity demand forecasting (Pappas et al 2008). However, the time-series analysis usually only depends on historical data to forecast with a somewhat arbitrage assumption that past pattern will definitely persist in the future.…”

Section: Introductionmentioning

confidence: 99%

Forecasting residential electricity demand in provincial China

Liao

Liu

Gao

et al. 2016

Environ Sci Pollut Res

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In China, more than 80% electricity comes from coal which dominates the CO2 emissions. Residential electricity demand forecasting plays a significant role in electricity infrastructure planning and energy policy designing, but it is challenging to make an accurate forecast for developing countries. This paper forecasts the provincial residential electricity consumption of China in the 13th Five-Year-Plan (2016-2020) period using panel data. To overcome the limitations of widely used predication models with unreliably prior knowledge on function forms, a robust piecewise linear model in reduced form is utilized to capture the non-deterministic relationship between income and residential electricity consumption. The forecast results suggest that the growth rates of developed provinces will slow down, while the less developed will be still in fast growing. The national residential electricity demand will increase at 6.6% annually during 2016-2020, and populous provinces such as Guangdong will be the main contributors to the increments.

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Section: Introductionmentioning

confidence: 99%

Forecasting residential electricity demand in provincial China

Liao

Liu

Gao

et al. 2016

Environ Sci Pollut Res

View full text Add to dashboard Cite

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“…We have also chosen the ANN models that have been used extensively in the GEM (e.g., [14,44,45]), with known pros and cons. SARIMAX models however have not been used (although ARMA and ARIMA ones have been applied) [126]. The results indicate that the PC-regression method produces forecasts superior to the ones by the conventional, Holt-Winters', ETS, ANN and SVM models (when applied to daily, day-ahead, aggregated load data).…”

Section: Discussionmentioning

confidence: 77%

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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“…Many operations such as electricity generation control, energy planning, and security studies are based on STLF. Table 3 gives particularly a comparison between the ANN, ARIMA, and fuzzy logic methods used for STLF [30][31][32][33][34][35][36]. A review of literature highlights that the fuzzy logic approach is both sufficiently efficient and versatile to meet the expectations defined at the beginning of the article.…”

Section: The Fuzzy Logic As a Versatile Methods Used To Predict Electrmentioning

confidence: 99%

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

2017

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Electricity demand shifting and reduction still raise a huge interest for end-users at the household level, especially because of the ongoing design of a dynamic pricing approach. In particular, end-users must act as the starting point for decreasing their consumption during peak hours to prevent the need to extend the grid and thus save considerable costs. This article points out the relevance of a fuzzy logic algorithm to efficiently predict short term load consumption (STLC). This approach is the cornerstone of a new home energy management (HEM) algorithm which is able to optimize the cost of electricity consumption, while smoothing the peak demand. The fuzzy logic modeling involves a strong reliance on a complete database of real consumption data from many instrumented show houses. The proposed HEM algorithm enables any end-user to manage his electricity consumption with a high degree of flexibility and transparency, and "reshape" the load profile. For example, this can be mainly achieved using smart control of a storage system coupled with remote management of the electric appliances. The simulation results demonstrate that an accurate prediction of STLC gives the possibility of achieving optimal planning and operation of the HEM system.

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Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models

Cited by 267 publications

References 36 publications

Forecasting residential electricity demand in provincial China

Forecasting residential electricity demand in provincial China

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

The Fuzzy Logic Method to Efficiently Optimize Electricity Consumption in Individual Housing

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