A PSO algorithm-based seasonal nonlinear grey Bernoulli model with fractional order accumulation for forecasting quarterly hydropower generation

Jiang, Jianming; Wu, Wei; Li, Qi; Zhang, Yu

doi:10.3233/jifs-200113

Cited by 15 publications

(4 citation statements)

References 32 publications

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“…SFANGBM(1,1) can be used to predict seasonal (e.g., monthly or quarterly) data and to estimate annual data by equating the value of the seasonal fluctuation index (

) to 1. Additionally, the proposed model is different from that of Jiang et al [64] and based on a GA.…”

Section: Introductionmentioning

confidence: 87%

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods

2021

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“…SFANGBM(1,1) can be used to predict seasonal (e.g., monthly or quarterly) data and to estimate annual data by equating the value of the seasonal fluctuation index (

) to 1. Additionally, the proposed model is different from that of Jiang et al [64] and based on a GA.…”

Section: Introductionmentioning

confidence: 87%

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods

2021

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“…Tus, optimizing the method of generating background values is vital for improving the forecast precision of GM (1, 1) through searching for the ideal λ * value within equation ( 7) [17]. Te PSO algorithm of Kennedy and Eberhart (1995) is a population-based algorithm that randomly adjusts the velocity of a population of individuals to search a multidimensional space for a global optimum [28]. It converges faster and requires fewer adjustable parameters.…”

Section: Building the Gm (1 1) Modelmentioning

confidence: 99%

Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization

Ren

Feng

Gao

et al. 2023

Journal of Mathematics

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Predicting telemetry data is vital for the proper operation of orbiting spacecraft. The Grey–Markov model with sliding window (GMSW) combines Grey model (GM (1, 1)) and Markov chain forecast model, which allows it to describe the fluctuation of telemetry data. However, the Grey–Markov model with sliding window does not provide better predictions of telemetry series with the pseudo-periodic phenomenon. To overcome this drawback, we improved the GMSW model by applying particle swarm optimization (PSO) algorithm a sliding window for better prediction of spacecraft telemetry data (denoted as PGMSW model). In order to produce more accurate predictions, background-value optimization is specially carried out using the particle swarm optimization technique in conventional GM (1, 1). For verifying PGMSW, it is utilized in the prediction of the cyclic fluctuation of telemetry series data and exponential variations therein. The simulation results indicate that the PGMSW model provides accurate solutions for prediction problems similar to the pseudo-periodic telemetry series.

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“…Because of the seasonal change of residential solar energy consumption in the United States, Wang et al predicted the residential solar energy consumption in the United States on the premise of grouping the data sequence according to seasonal characteristics and using the grey prediction model to predict respectively (Wang et al, 2019). In terms of seasonality represented by the seasonal index, Jiang et al established a fractional‐order nonlinear grey Bernoulli model based on removing seasonality with the moving average method to predict seasonal hydroelectric power generation (Jiang et al, 2021). Wang et al constructed a new grey prediction model with dynamic seasonal adjustment based on the exponential smoothing method, considering the dynamic seasonal fluctuation of monthly solar energy consumption in the United States (Wang et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent grey forecasting model based on periodic aggregation generating operator and its application in forecasting clean energy

Sui

Qian

2021

Expert Systems

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Accurate prediction of long‐term and short‐term clean energy production is the basis for understanding short‐term clean energy supply capacity, long‐term clean energy development trend and evaluating the effect of energy policies. However, under the circumstances of the large time span, the insufficient data samples and the periodic characteristics of seasonal clean energy production make the traditional grey prediction model prone to produce forecasting deviations. Given this situation, a novel seasonal fractional‐order full‐order time power discrete grey prediction model is initially proposed to deal with long‐term clean energy production sequences featured with nonlinearity and periodicity. Based on the proposed model, we also propose a data‐based algorithm to select the model structure adaptively. To prove the practicability of the new model for nonlinear long‐term development trend, monthly periodic time series and quarterly periodic time series, this article uses the new model to predict annual hydropower capacity in North America, monthly natural gas production in China and quarterly solar power generation in China. And the prediction results are compared with the existing grey models and non‐grey prediction models. Different methods including GM (1,1), DGM (1,1), NGM (1,1), ARGM (1,1), ENGM (1,1), Verhulst, CCRGM (1,1), FOTP‐DGMr (1,1), PFSM (1,1), Holt‐winters model, SARIMA model, SGM, HP‐GM and DGGM are used as benchmarks. In experiments, the MAPE of the proposed model is 2.92%, 2.43%, and 7.87%, respectively. The results of empirical analysis indicate that the proposed model generally outperform the benchmark model as it can well capture nonlinear long‐term development trend and seasonal characteristics.

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A PSO algorithm-based seasonal nonlinear grey Bernoulli model with fractional order accumulation for forecasting quarterly hydropower generation

Cited by 15 publications

References 32 publications

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods

Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods

Predicting Spacecraft Telemetry Data by Using Grey–Markov Model with Sliding Window and Particle Swarm Optimization

Intelligent grey forecasting model based on periodic aggregation generating operator and its application in forecasting clean energy

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