STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

Tebong, Njogho Kenneth; Simo, Théophile; Takougang, Armand Nzeukou; Ntanguen, Patrick Herve

doi:10.1016/j.heliyon.2023.e16456

Cited by 15 publications

(2 citation statements)

References 32 publications

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“…As a versatile method, STL has an advantage over other decomposition methods on account of its ability to deal with any type of seasonality and to be robust to outliers. [28,29] It is based on locally weighted regression (LOESS), a sequence of smoothing procedures. [26] STL consists of two recursive processes: an inner loop nested an outer loop.…”

Section: Seasonal Trend Lossesmentioning

confidence: 99%

Dynamic changes of net anthropogenic nitrogen/phosphorus input and its riverine export in the Beijing's subcenter

Luo,

Peng,

Shang

et al. 2024

CLEAN Soil Air Water

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Due to human activities, the overabundance of regional nitrogen/phosphorus (N/P) has a huge impact on ecological environment worldwide. It is necessary to comprehensively understand the human‐induced N/P input and spatiotemporal variations of nutrients in a river. As the adjustment to the spatial structure of Beijing, the Beijing's subcenter is an important strategic position in promoting the coordinated development of Beijing–Tianjin–Hebei. In this study, the dynamic changes of ammonia nitrogen (NH3‐N) and total phosphorus (TP) from 2012 to 2022 were analyzed, then the net anthropogenic N input (NANI) and net anthropogenic P input (NAPI) was estimated in the Beijing's subcenter. The results showed that both NH3‐N and TP had an overall downward trend with a three‐stage pattern. The NANI and NAPI declined from 5.68 × 104 and 0.95 × 104 to 0.78 × 104 and 0.15 × 104 kg km−2 per year, respectively. The chemical fertilizers input accounted for the largest part at 53–81% of NANI and 49–80% of NAPI. There were high values of N and P input in the southeast of Tongzhou, and the fluxes of N and P were positively correlated with the input of anthropogenic N and P in the Chaobai River Basin (Tongzhou District) and the Beiyun River Basin (Tongzhou District). This study would provide the foundation for setting priorities and enacting more targeted N/P management strategies in the Beijing's subcenter.

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Section: Seasonal Trend Lossesmentioning

confidence: 99%

Dynamic changes of net anthropogenic nitrogen/phosphorus input and its riverine export in the Beijing's subcenter

Luo,

Peng,

Shang

et al. 2024

CLEAN Soil Air Water

View full text Add to dashboard Cite

show abstract

“…The hybrid models recorded higher accuracy than other independent models even without preprocessing. Tebong et al [49] used deep learning models to create ensembles. STL decomposition decomposed reservoir inflows and precipitation into random, seasonal, and trend components.…”

Section: Related Workmentioning

confidence: 99%

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

Stefenon

Seman

Neto

et al. 2023

Sensors

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Disruptive failures threaten the reliability of electric supply in power branches, often indicated by the rise of leakage current in distribution insulators. This paper presents a novel, hybrid method for fault prediction based on the time series of the leakage current of contaminated insulators. In a controlled high-voltage laboratory simulation, 15 kV-class insulators from an electrical power distribution network were exposed to increasing contamination in a salt chamber. The leakage current was recorded over 28 h of effective exposure, culminating in a flashover in all considered insulators. This flashover event served as the prediction mark that this paper proposes to evaluate. The proposed method applies the Christiano–Fitzgerald random walk (CFRW) filter for trend decomposition and the group data-handling (GMDH) method for time series prediction. The CFRW filter, with its versatility, proved to be more effective than the seasonal decomposition using moving averages in reducing non-linearities. The CFRW-GMDH method, with a root-mean-squared error of 3.44×10−12, outperformed both the standard GMDH and long short-term memory models in fault prediction. This superior performance suggested that the CFRW-GMDH method is a promising tool for predicting faults in power grid insulators based on leakage current data. This approach can provide power utilities with a reliable tool for monitoring insulator health and predicting failures, thereby enhancing the reliability of the power supply.

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A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment

Jia,

Zhang,

Shi

et al. 2024

Environ Sci Pollut Res

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STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

Cited by 15 publications

References 32 publications

Dynamic changes of net anthropogenic nitrogen/phosphorus input and its riverine export in the Beijing's subcenter

Dynamic changes of net anthropogenic nitrogen/phosphorus input and its riverine export in the Beijing's subcenter

Group Method of Data Handling Using Christiano–Fitzgerald Random Walk Filter for Insulator Fault Prediction

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment

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