A survey on wind energy, load and price forecasting: (Forecasting methods)

Nagaraja, Y. R.; Devaraju, T. C.; Kumar, Manish; Madichetty, Sreedhar

doi:10.1109/iceeot.2016.7754792

Cited by 16 publications

(9 citation statements)

References 34 publications

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“…8,13,14 Nevertheless, probabilistic, statistical, and artificial intelligence approaches can be considered as the most frequently used. 8 A concise description of most common models is presented in the following sections.…”

Section: Forecast Modelsmentioning

confidence: 99%

“…9 In addition, Pm is widely used as a comparison in order to validate any proposed FM related to highly random phenomena, as seen in several works. 4,8,9,[15][16][17][18][19][20][21][22] For very short and short-term forecasts, persistence is the most used due to its simplicity as benchmark since it does not need even the calculation of coefficients. We can highlight the work of Koc xak, 23 where the authors use the persistence model as a benchmark to compare hourly energy forecast models of a wind turbine with many forecast horizons (up to 24-step-forward).…”

Section: Persistencementioning

confidence: 99%

“…To select the approach to predict wind turbine, it is important to clarify that every case of study has its own characteristics and goals. In Nagaraja et al, 8 the authors made a review of approaches to forecast separately wind speed/power, electrical load, and energy prices. It was found that for each variable, artificial intelligence, fuzzy theory, regression methods, numeric weather prediction (NWP), and statistical methods were employed.…”

Section: Introductionmentioning

confidence: 99%

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Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García

Calderón

González-A

et al. 2019

Advances in Mechanical Engineering

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Forecast models for wind speed and wind turbine power generation are valuable support tools for operators of Control Energy Center. In this work, a year of daily energy output of a wind turbine is analyzed. The original time series was separated into a high-power sample and a low-power sample. High-power sample has a seasonal pattern while lowpower sample does not. Afterward, a sARIMA model was produced for high-power sample forecast, with a good performance, while for low-power sample any ARIMA model defeated persistence model; thus, a couple of nonlinear autoregressive artificial neural networks are proposed. Mean absolute error and mean square error are reported and demonstrate that the sARIMA model can predict satisfactorily high-power sample, even with limited data, while to forecast low-power sample, it is necessary to use a neural networks approach and all data available to produce accurate forecasts. In each case, a normalized comparison with persistence model is also reported. Finally, a method which uses previous data of daily output energy and forecasted future wind speed values from a numeric weather prediction model is presented to objectively identify whether the current time is in a high-power or low-power regime to choose the ad hoc daily output energy forecast model.

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Section: Forecast Modelsmentioning

confidence: 99%

Section: Persistencementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

García

Calderón

González-A

et al. 2019

Advances in Mechanical Engineering

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“…Electricity load forecasting is a process of predicting future load changes by analyzing historical load data. It explores the dynamic changes of load data by qualitative and quantitative methods, such as statistics, computer science, and empirical analysis [2]. Based on the time horizon of prediction, the load forecasting can be classified into four categories: long-term forecasting, medium-term forecasting, short-term forecasting, and ultra-short-term forecasting [3].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

et al. 2019

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Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

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“…in concern. There are numerous efforts and forecasting methods related to wind and PV power plants outputs and some of them are well developed [8], [9]. At the same time there is at some extent unavoidable forecasting errors (difference between forecasted and realized generation) in RES generation with intrinsic variability (such as wind and PV generation).…”

Section: Introductionmentioning

confidence: 99%

Utjecaj revitalizacije postojećih i izgradnje novih hidroelektrana na neometanu penetraciju OIE: regionalni slučaj zemalja JI Europe

Rajšl¹,

Klenkar²

2018

Proceedings of the 31st International Congress on Process Industry

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The global problem in the form of climate change is increasingly evident. The consensus of the world's scientific and expert community is that today's human action, primarily regarding greenhouse gas emissions, is one of the key reasons for accelerating the negative effects, primarily the rise in average temperature on Earth. One of the solutions to reduce CO2 emissions from the power generation sector, which is one of the most significant greenhouse gas emitters, is the intensified penetration of renewable energy sources. This paper will examine the impact of increasing capacities in reservoir and pumped storage hydropower plants, as well as the revitalization of existing hydropower plants on the possibility of unconstrained penetration of renewable energy sources, primarily wind, based on the developed regional model. Namely, due to its intermittent nature wind energy requires additional flexibility in the system, and hydro power plants are one of the best providers of such services currently, especially in the region of SE Europe. On the other hand, hydropower plants usually have high investment costs.

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A survey on wind energy, load and price forecasting: (Forecasting methods)

Cited by 16 publications

References 34 publications

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

Forecast of daily output energy of wind turbine using sARIMA and nonlinear autoregressive models

Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Utjecaj revitalizacije postojećih i izgradnje novih hidroelektrana na neometanu penetraciju OIE: regionalni slučaj zemalja JI Europe

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