A novel ensemble model for long-term forecasting of wind and hydro power generation

Malhan, Priyanka; Mittal, M.

doi:10.1016/j.enconman.2021.114983

Cited by 44 publications

(9 citation statements)

References 61 publications

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“…Integrated power generation systems using different sources are a better solution than relying on a single option; wind and hydropower have synergy and flexibility [26,27]. Small hydropower production can be combined with solar energy.…”

Section: Background Literaturementioning

confidence: 99%

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Lincaru,

Grigorescu,

Dincer

2024

Processes

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Renewable sources of energy production are some of the main targets today to protect the environment through reduced fossil fuel consumption and CO2 emissions. Alongside wind, solar, marine, biomass and nuclear sources, hydropower is among the oldest but still not fully explored renewable energy sources. Compared with other sources like wind and solar, hydropower is more stable and consistent, offering increased predictability. Even so, it should be analyzed considering water flow, dams capacity, climate change, irrigation, navigation, and so on. The aim of this study is to propose a forecast model of hydropower production capacity and identify long-term trends. The curve fit forecast parabolic model was applied to 33 European countries for time series data from 1990 to 2021. Space-time cube ArcGIS representation in 2D and 3D offers visualization of the prediction and model confidence rate. The quadratic trajectory fit the raw data for 14 countries, validated by visual check, and in 20 countries, validated by FMRSE 10% threshold from the maximal value. The quadratic model choice is good for forecasting future values of hydropower electric capacity in 22 countries, with accuracy confirmed by the VMRSE 10% threshold from the maximal value. Seven local outliers were identified, with only one validated as a global outlier based on the Generalized Extreme Studentized Deviate (GESD) test at a 5% maximal number of outliers and a 90% confidence level. This result achieves our objective of estimating a level with a high degree of occurrence and offering a reliable forecast of hydropower production capacity. All European countries show a growing trend in the short term, but the trends show a stagnation or decrease if policies do not consider intensive growth through new technology integration and digital adoption. Unfortunately, Europe does not have extensive growth potential compared with Asia–Pacific. Public policies must boost hybrid hydro–wind or hydro–solar systems and intensive technical solutions.

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Section: Background Literaturementioning

confidence: 99%

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Lincaru,

Grigorescu,

Dincer

2024

Processes

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“…Different from the WPGF, Alizamir et al (2020) have made solar radiation forecasts for four different locations in Turkey and the USA using machine learning methods and concluded that gradient boosting trees outperform all the other models. Malhan and Mittal (2022) have proposed a long-term forecasting model for wind and hydropower generations of five states in India by using 11 years long and daily basis average, peak hour, off-peak hour wind, and hydro generation data. While their methods consist of three phases which use the diligent search algorithm and a hybrid of ARIMA and bidirectional LSTM, the proposed model has outperformed the results of the individual models especially for the time horizons from quarter year to year.…”

Section: Related Workmentioning

confidence: 99%

“…Malhan and Mittal (2022) have proposed a long-term forecasting model for wind and hydropower generations of five states in India by using 11 years long and daily basis average, peak hour, off-peak hour wind, and hydro generation data. While their methods consist of three phases which use the diligent search algorithm and a hybrid of ARIMA and bidirectional LSTM, the proposed model has outperformed the results of the individual models especially for the time horizons from quarter year to year.…”

Section: Related Workmentioning

confidence: 99%

A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey

Özen

Deniz

2022

Wind Engineering

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A country-based day-ahead wind power generation forecast (WPGF) model with a grid selection algorithm and feature selection models was proposed in this study. Atmospheric variables extracted from 300, 500, 700 hPa pressure levels, and surface level of ERA5 reanalysis data with 2.5° spatial resolution were used to train/validate the categorical boosting (CatBoost) model. A special grid selection algorithm was proposed by considering Turkey’s spatial distribution of wind power plants. The day-ahead forecasts of ECMWF’s HRES (High-resolution) were used as the test subset, therefore, paving the way for the operational use of the model. The proposed model could be considered much as a specialized machine learning based downscaling method for country-based WPGF due to using numerical weather prediction model outputs as its input. Results showed that the proposed model that uses fewer features has outperformed the other models with a normalized root mean square error of 7.6% and coefficient of determination of 0.8989.

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“…A model combining Wavelet and Autoregressive Integrated Moving Average model (ARIMA) was established [3] and the prediction accuracy by processing wind power data through wavelet decomposition was improved. A combination of Bidirectional Long Short-Term Memory (Bi-LSTM) and DSA-ARIMA is used to obtain data features [4] , which are then analyzed using the Diligent Search Algorithm (DSA). The simulation plots and data demonstrate that this model enables the accuracy of the predictions to be improved.…”

Section: Introductionmentioning

confidence: 99%

Short-term wind power prediction based on DWT-PSO-LSTM

Zong

Gao

2023

J. Phys.: Conf. Ser.

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With the “carbon peaking and carbon neutrality” target, quite a few people are focusing on the development of renewable energy, especially wind power, which is the largest source of renewable energy generation and is still expanding. In aiming to make the wind power forecasts more accurate by limiting the errors between the predicted and actual values, a model combining Particle Swarm Optimization, Discrete Wavelet Transform, and Long Short-Term Memory (DWT-PSO-LSTM) is developed to predict the wind power for short periods. First, the wind power is decomposed and normalized by DWT. Then, each decomposition result is input into LSTM prediction, and the model with the good result is obtained by finding the number of nodes and learning the efficiency of the best LSTM through PSO. Finally, the full prediction results are output and then all components are summed. Simulations using open wind power data from Yalova province, Turkey, indicate that the model advanced in this article can reduce the prediction errors better than other models.

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A novel ensemble model for long-term forecasting of wind and hydro power generation

Cited by 44 publications

References 61 publications

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

A comprehensive country-based day-ahead wind power generation forecast model by coupling numerical weather prediction data and CatBoost with feature selection methods for Turkey

Short-term wind power prediction based on DWT-PSO-LSTM

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