Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

Drakaki, Korina-Konstantina; Sakki, Georgia-Konstantina; Tsoukalas, Ioannis; Kossieris, Panagiotis; Efstratiadis, Andreas

doi:10.5194/adgeo-56-155-2022

Cited by 11 publications

(2 citation statements)

References 20 publications

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“…Small-scale hydropower sources are highly appreciated as a mass local sustainable solution. Drakaki et al [39] propose a day-ahead forecast for a small hydro plant to reveal its potential or hydropower scheduling [40]. Jurasz et al [41] developed a day-ahead model for a hydro-wind hybrid system.…”

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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“…However, it is hard for the ANN model to learn the long-term relationship between rainfall and power generation since the model has limited complexity. Drakaki et al [6] incorporated the expert's knowledge about the hydrological regime and the technical characteristics of the SHPP within the power generation modeling process, which proves to be the more advantageous method. However, in reality, hydrological data is not directly correlated with SHPPs in space, i.e., they are weakly coupled, which means that hydrological data cannot be directly converted into the output power of the SHPP.…”

Section: Introductionmentioning

confidence: 99%

A Weak-Coupling Flow-Power Forecasting Method for Small Hydropower Station Group

Long

Wei

et al. 2023

International Journal of Energy Research

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Due to the need for rural revitalization and renewable energy utilization, a large quantity of small hydropower stations is emerging, with weak-coupling flow-power features. However, a weak spatial coupling exists between the distribution of small hydropower station groups (SHSGs) and gaging stations since the small hydropower stations are usually located in remote areas lacking hydrographic facilities. That may cause weak or no coupling between the hydroregime and the power output of small hydropower plants in the target basin, thus hindering accurate power forecasting. To meet the need for short-term power generation prediction for SHSGs in intensive management areas, we propose a data-driven power-forecasting model which can mine the correlation information of weakly coupled basins while transferring hydrological knowledge to uncoupled basins. First, to make the task data domains before and after migration more similar, a similar watershed matching algorithm based on the nonlinear dimensionality reduction algorithm (Isomap) and the k -means++ algorithm is proposed; then a short-term interpretable runoff prediction model is pretrained, and features are extracted in the source basin using the temporal fusion transformer (TFT) network. After that, a heuristic ensemble fine-tuning model based on the k -fold cross-validation fine-tuning method and heuristic ensemble algorithm is proposed to transfer the public knowledge of the source basin to the uncoupled basin. Then, a TFT network is used to mine the weak-coupling relationship between the hydrological regime and the output power of an SHSG. Finally, the validity of the model is verified with an example from a European region. After considering the weakly coupled flow-power characteristics, the mean absolute percentage error (MAPE) of three SHSGs’ power prediction by the proposed method is on average 34.8% lower than that of the method without considering the hydrological information.

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Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

Sahin,

Ozbay Karakus

2024

Neural Comput & Applic

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Renewable energy sources and hydroelectric power generation in large parts of the electricity market are crucial as environmental pollution worsens . Utilizing meteorological data from the region, where the Hirfanlı Dam is located, this study employs machine learning (ML) and introduces a novel hybrid Genetic Grey Wolf Optimizer (GGW0)-based Convolutional Neural Network/Recurrent Neural Network (CNN/RNN) regression technique to predict hydroelectric power production (PP). In the first section of the study, various ML techniques SVR (Support Vector Regression), ELM (Extreme Learning Machine), RFR (Random Forest Regression), ANN (Artificial Neural Networks) and WKNNR (Weighted K-Nearest Neighbor) are presented with the Principal Component Analysis (PCA) method and the minimum–maximum method in the normalization of the features. A novel GGWO and CNN/RNN model)-Long Short-Term Memory (LSTM) regression technique is introduced in the second section. GGWO is used to select features, while the proposed CNN/RNN-LSTM model is employed for feature extraction and prediction of PP. The study demonstrates that the ELM algorithm in Method I outperforms other ML models, achieving a correlation coefficient (r) of 0.977 and the mean absolute error (MAE) of 0.4 with the best feature subset. Additionally, the proposed CNN/RNN hybrid model in Method II yields even better results, with r and MAE values of 0.9802 and 0.314, respectively. The research contributes to the field of renewable energy prediction, and the results can aid in efficient decision making for electricity generation and resource management.

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Day-ahead energy production in small hydropower plants: uncertainty-aware forecasts through effective coupling of knowledge and data

Cited by 11 publications

References 20 publications

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

Parabolic Modeling Forecasts of Space and Time European Hydropower Production

A Weak-Coupling Flow-Power Forecasting Method for Small Hydropower Station Group

Smart hydropower management: utilizing machine learning and deep learning method to enhance dam’s energy generation efficiency

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