Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

Unnikrishnan, Poornima; Jothiprakash, V.

doi:10.1016/j.jhydrol.2018.04.032

Cited by 47 publications

(10 citation statements)

References 43 publications

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“…SSA, as a time series analysis tool used to decompose an original time sequence into interpretable components, has been applied in many fields, including biology, physics, climatology, and economics [35][36][37][38]. The decomposition process can be divided into four steps:…”

Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

et al. 2019

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Wind speed forecasting helps to increase the efficacy of wind farms and prompts the comparative superiority of wind energy in the global electricity system. Many wind speed forecasting theories have been widely applied to forecast wind speed, which is nonlinear, and unstable. Current forecasting strategies can be applied to various wind speed time series. However, some models neglect the prerequisite of data preprocessing and the objective of simultaneously optimizing accuracy and stability, which results in poor forecast. In this research, we developed a combined wind speed forecasting strategy that includes several components: data pretreatment, optimization, forecasting, and assessment. The developed system remedies some deficiencies in traditional single models and markedly enhances wind speed forecasting performance. To evaluate the performance of this combined strategy, 10-min wind speed sequences gathered from large wind farms in Shandong province in China were adopted as a case study. The simulation results show that the forecasting ability of our proposed combined strategy surpasses the other selected comparable models to some extent. Thus, the model can provide reliable support for wind power generation scheduling.

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Section: Singular Spectrum Analysis (Ssa)mentioning

confidence: 99%

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

et al. 2019

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“…16–18 Since then, it has attracted a lot of attention. It has become a standard tool in signal processing 19 with a great number of applications in climatology, 14,20,21 neuro science, 22–24 as well as forecasting. 25 Comparing SSA to other forecasting methods such as AutoRegressive Integrated Moving Average (ARIMA) and Holt–Winters showed that SSA significantly outperforms the ARIMA and Holt–Winters’s methods on the long run.…”

Section: Ssamentioning

confidence: 99%

A benchmark and real-time estimator for the passenger arrival rate to elevator system

Hammoudeh

Al-Sharif

Al-Shabi

2018

Building Services Engineering Research and Technology

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Arrival rate is the number of passengers arriving for elevator service in a certain period of time. Arrival rate is fundamental in expressing the heaviness of the traffic. Hence, it is vital for determining the required number of elevators and the specifications of each elevator such as the speed, capacity, and sector sizes. The passenger arrival process is a random process that is full of noise, and a processing step is required to extract the arrival rate from recorded arrival times of passengers. This work develops a real-time estimator and a benchmark for estimating the arrival rate. There are three contributions in this work; the first is suggesting a benchmark for estimating arrival rate; singular spectrum analysis extracts the arrival rate from noisy data. Hence, singular spectrum analysis is suggested as a benchmark for evaluating the performance of other algorithms. Even though singular spectrum analysis is powerful in extracting the arrival rate, it is not convenient for updating the arrival rate in real time. The second contribution is developing a real-time estimator for the passenger arrival rate that updates its parameters dynamically; dynamic exponentially weighted moving average was developed to provide instantaneous arrival rate updates. The third contribution is introducing exponentially weighted moving average as a linear model for passenger arrival, which opens the door to a large number of model-based algorithms in control theory; Kalman filtering was developed in this work on the top of the EWMA linear model. The results of applying Kalman filtering and DEWMA to real-life data show them as efficient methods for estimating passenger arrival rate to the elevators in real time. Practical application: The methods presented in this paper would allow an elevator controller designer to detect the intensity of the passenger arrival rate. By doing this, it is possible for the elevator controller to switch between different group control algorithms. For example, it could decide to switch from conventional group control to sectoring control and vice versa.

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“…Pre-processing and post-processing techniques have been widely applied to improve the forecast accuracy of hydrological models. Pre-processing techniques, such as the wavelet transform (Barzegar et al 2018), empirical mode decomposition (Wu et al 2017), singular spectrum analysis (Poornima & Jothiprakash 2018) are used to remove outliers or noise from the raw data. Post-processing techniques, such as Kalman filtering (Kalman 1960;Liu et al 2016), Bayesian model averaging (Raftery et al 2005;Li et al 2017), hydrologic uncertainty processor (Krzysztofowicz 1999;Han et al 2019) are used to correct the forecast values in real time.…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

et al. 2021

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The conceptual hydrologic model has been widely used for flood forecasting, while long short-term memory (LSTM) neural network has been demonstrated a powerful ability to tackle time-series predictions. This study proposed a novel hybrid model by combining the Xinanjiang (XAJ) conceptual model and LSTM model (XAJ-LSTM) to achieve precise multi-step-ahead flood forecasts. The hybrid model takes flood forecasts of the XAJ model as the input variables of the LSTM model to enhance the physical mechanism of hydrological modeling. Using the XAJ and the LSTM models as benchmark models for comparison purposes, the hybrid model was applied to the Lushui reservoir catchment in China. The results demonstrated that three models could offer reasonable multi-step-ahead flood forecasts and the XAJ-LSTM model not only could effectively simulate the long-term dependence between precipitation and flood datasets, but also could create more accurate forecasts than the XAJ and the LSTM models. The hybrid model maintained similar forecast performance after feeding with simulated flood values of the XAJ model during horizons to . The study concludes that the XAJ-LSTM model that integrates the conceptual model and machine learning can raise the accuracy of multi-step-ahead flood forecasts while improving the interpretability of data-driven model internals.

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Daily rainfall forecasting for one year in a single run using Singular Spectrum Analysis

Cited by 47 publications

References 43 publications

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

Integrated Forecasting Method for Wind Energy Management: A Case Study in China

A benchmark and real-time estimator for the passenger arrival rate to elevator system

A novel hybrid XAJ-LSTM model for multi-step-ahead flood forecasting

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