Residential Electricity Consumption Forecasting Using a Geometric Combination Approach

Júnior, Luiz Albino Teixeira; Menezes, Moisés Lima de; Cassiano, Keila Mara; Pessanha, José Francisco Moreira; Souza, Reinaldo Castro

doi:10.1142/s2335680413500087

Cited by 13 publications

(4 citation statements)

References 18 publications

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“…Some researchers forecast time series in several domains using Box-Jenkins method or by combining it with other methods. Internationally, JUNIOR et al [6] combined the Box-Jenkins method with wavelet analysis or singular spectrum analysis to form into two ap-proaches and performed hybrid forecasting for the residential consumption of electricity in Brazil; Fard and Akbari-Zadeh [7] proposed a forecasting method that hybridizes wavelet transform, ARIMA and Artificial neural network (ANN) for short-term electricity load forecasting. Nationally, Ye et al [8] developed a five-step modeling approach to analyze and predict the absolute surface temperature, which modeled the stochastic component following the Box-Jenkins method.…”

Section: Introductionmentioning

confidence: 99%

Self‐adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

Liu

Peng

2017

Chin. j. electron.

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Section: Introductionmentioning

confidence: 99%

Self‐adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

Liu

Peng

2017

Chin. j. electron.

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“…SSA is mainly considered as a filtering method as it seeks to decomposes a series into its component parts, and reconstructs the series by leaving the random noise component behind prior to using the newly reconstructed less noisy series for forecasting future data points [8][9][10]. Recently, the SSA technique has been increasingly applied in the field of energy (see for example, [11][12][13][14][15][16][17][18]). This increased application of SSA in energy further warrants the conduct of this research which seeks to identify the impact of outliers on SSA based energy forecast.…”

Section: Introductionmentioning

confidence: 99%

Forecasting energy data using Singular Spectrum Analysis in the presence of outlier(s)

Ghodsi¹,

Omer²

2014

Int. J. Energy Stat.

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The aim of this paper is to present a comparative study on the performance of the two different forecasting approaches of SSA in the presence of outliers. We examine this issue from different points of view. As our real data set, we have considered the well known WTI Spot Price series. The effect on forecasting process when confronted with outlier(s) in different parts of a time series is evaluated. Based on this study, we find evidence which suggests that the existence of outliers affect SSA reconstruction and forecasting results, and that VSSA forecasting performs better than RSSA in terms of the accuracy and robustness of forecasts.

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“…The roots of SSA are closely associated with Broomhead and King [34,35]. In recent years, SSA has been applied successfully in solving many practical problems (see, for example, [8] and [36][37][38][39][40][41][42]). …”

Section: Introductionmentioning

confidence: 99%

Evaluating the effectiveness of parametric and nonparametric energy consumption forecasts for a developing country

Silva¹,

Rajapaksa²

2014

Int. J. Energy Stat.

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This paper seeks to analyse and evaluate Sri Lanka's energy consumption forecasts, more specifically, electricity, petroleum, coal and renewable electricity consumption using a variety of parametric and nonparametric forecasting techniques. The Sri Lankan economy is emerging following the end of a prolonged civil war, and thus this topic is opportune as accurate forecasts of energy requirements are indispensable for sustaining the ongoing rapid economic expansion. We also consider evaluating the appropriateness of parametric and nonparametric energy forecasting methods for Sri Lanka. In addition, this paper marks the introductory application of SSA for forecasting in Sri Lanka, and the results from SSA are compared against the popular benchmarks of ARIMA, ETS, HW, TBATS, and NN. We find statistically significant evidence proving that the SSA model outperforms ETS and HW at forecasting renewable electricity consumption, and also, that the SSA model outperforms ARIMA and TBATS models at forecasting coal consumption. On average, the SSA model is found to be best for energy consumption forecasting in Sri Lanka whilst the Neural Networks model is second best

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Residential Electricity Consumption Forecasting Using a Geometric Combination Approach

Cited by 13 publications

References 18 publications

Self‐adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

Self‐adaptive Processing and Forecasting Algorithm for Univariate Linear Time Series

Forecasting energy data using Singular Spectrum Analysis in the presence of outlier(s)

Evaluating the effectiveness of parametric and nonparametric energy consumption forecasts for a developing country

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