Evaluation and Analysis of Electric Power in China Based on the ARMA Model

Yu, Lei

doi:10.1155/2022/5017751

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…Moreover, energy forecasting is usually based on irregular and nonlinear data in real life. Yu and Yang (2022) used China's electricity demand dataset spanning from 2004 to 2019. They combined the dataset with the ARMA model, to analyze the future electricity situation in China and accurately predict China's electricity demand in 2020.…”

Section: Literature Reviewmentioning

confidence: 99%

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Duan,

Zhang,

Wang

2023

Front. Energy Res.

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As a clean fossil energy source, natural gas plays a crucial role in the global energy transition. Forecasting natural gas prices is an important area of research. This paper aims at developing a novel hybrid model that contributes to the prediction of natural gas prices. We develop a novel hybrid model that combines the “Decomposition Algorithm” (CEEMDAN), “Ensemble Algorithm” (Bagging), “Optimization Algorithm” (HHO), and “Forecasting model” (SVR). The hybrid model is used for monthly Henry Hub natural gas forecasting. To avoid the problem of data leakage caused by decomposing the whole time series, we propose a rolling decomposition algorithm. In addition, we analyzed the factors affecting Henry Hub natural gas prices for multivariate forecasting. Experimental results indicate that the proposed model is more effective than the traditional model at predicting natural gas prices.

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Section: Literature Reviewmentioning

confidence: 99%

Henry Hub monthly natural gas price forecasting using CEEMDAN–Bagging–HHO–SVR

Duan,

Zhang,

Wang

2023

Front. Energy Res.

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“…In the remainder of this section, we perform various simulations to assess the efficacy of Pivot Clustering. We focus on ARMA(1,1) models as these do not require too much runtime for Pivot Clustering based on theoretical MSFE and are complex enough to describe demand data such as in [10]. Additionally, forecasting an aggregate of ARMA(1,1) demand sequence has been studied by [11], where forecasts were based on exponential smoothing.…”

Section: Ts Clustering Algorithms and Pivot Clustering; Empirical Eva...mentioning

confidence: 99%

Pivot Clustering to Minimize Error in Forecasting Aggregated Demand Streams Each Following an Autoregressive Moving Average Model

Kovtun,

Giloni,

Hurvich

et al. 2023

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In this paper, we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates; however, we show that if several aggregated clusters are formed in a structured manner, then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot algorithm, which we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases, such as aggregating demand generated by MA(1) models and aggregating demand generated by ARMA models with similar or the same parameters.

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