Hydroelectric Energy Forecast

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

doi:10.1142/s2335680413500142

Cited by 17 publications

(9 citation statements)

References 19 publications

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“…To obtain the seasonal component, the arithmetic mean of each Ripple subset is performed (11). The seasonality dataset is made by 7 terms repeated periodically, being the average value of the deviation between the real production and the trend.…”

Section: Hype Model and Hybrid Forecast Methodsmentioning

confidence: 99%

“…Data-driven models are nowadays the most widely adopted models in hydropower forecast [10]. Stochastic models such as ARIMA [11,12] or Persistence [13] are extensively used; in particular, the persistence model can be applied to RoR plants, which typically have high autocorrelation with the preceding day. Machine learning (ML) models are increasingly applied to hydrological forecast and have addressed and solved many issues in hydrological modeling [14].…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

et al. 2020

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The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

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Section: Hype Model and Hybrid Forecast Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

et al. 2020

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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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“…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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Hydroelectric Energy Forecast

Cited by 17 publications

References 19 publications

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network

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

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

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