Multi-step ahead forecasting for electric power load using an ensemble model

Zhao, Yubo; Guo, Ni; Chen, Wei; Zhang, Hailan; Guo, Bochao; Jia, Shuang; Tian, Zhen

doi:10.1016/j.eswa.2022.118649

Cited by 20 publications

(4 citation statements)

References 32 publications

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“…In this hybrid forecasting system, the optimization search process of the MVO algorithm used the Root Mean Square Error (RMSE) and the similarity (R) of the prediction curves to obtain the fitness value (NI) to determine the number of nodes in the implicit layer in the prediction model. In addition, the Root Mean Square (MAE), Mean Absolute Percentage Error (MAPE) and Nash-Sutcliffe efficiency coefficient (NSE) were selected, which together with RMSE and R to evaluate the predictive performance of the proposed model [44][45][46]. The parametric optimization of the MVO algorithmic process is shown in Figure 5.…”

Section: Hybrid Forecasting Systemmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid.

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Section: Hybrid Forecasting Systemmentioning

confidence: 99%

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

et al. 2022

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“…There has been extensive discussion in [1][2][3][4][5][6][7][8][9][10][11] of the conventional model approach for forecasting electrical time series data, including the ARIMA (autoregressive integrated moving average) and exponential smoothing. Meanwhile, numerous authors [12][13][14][15][16][17][18][19][20][21] explored machine learning approaches, including Prophet and neural networks (NN). The combination of the two approaches has also been widely explored (see [22][23][24][25][26]).…”

Section: Introductionmentioning

confidence: 99%

Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data

Sulandari,

Yudhanto,

Subanti

et al. 2023

Energies

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The importance of forecasting in the energy sector as part of electrical power equipment maintenance encourages researchers to obtain accurate electrical forecasting models. This study investigates simple to complex automatic methods and proposes two weighted ensemble approaches. The automated methods are the autoregressive integrated moving average; the exponential smoothing error–trend–seasonal method; the double seasonal Holt–Winter method; the trigonometric Box–Cox transformation, autoregressive, error, trend, and seasonal model; Prophet and neural networks. All accommodate trend and seasonal patterns commonly found in monthly, daily, hourly, or half-hourly electricity data. In comparison, the proposed ensemble approaches combine linearly (EnL) or nonlinearly (EnNL) the forecasting values obtained from all the single automatic methods by considering each model component’s weight. In this work, four electrical time series with different characteristics are examined, to demonstrate the effectiveness and applicability of the proposed ensemble approach—the model performances are compared based on root mean square error (RMSE) and absolute percentage errors (MAPEs). The experimental results show that compared to the existing average weighted ensemble approach, the proposed nonlinear weighted ensemble approach successfully reduces the RMSE and MAPE of the testing data by between 28% and 82%.

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“…Decomposing data [17,18]; • Feature selection [19,20]; • Clustering data [21]; • One or more forecasting models whose predictions are combined [22,23]; • A heuristic optimization algorithm to either estimate the models or their hyperparameters [24].…”

mentioning

confidence: 99%

Powering Electricity Forecasting with Transfer Learning

Kamalov,

Sulieman,

Moussa

et al. 2024

Energies

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Accurate forecasting is one of the keys to the efficient use of the limited existing energy resources and plays an important role in sustainable development. While most of the current research has focused on energy price forecasting, very few studies have considered medium-term (monthly) electricity generation. This research aims to fill this gap by proposing a novel forecasting approach based on zero-shot transfer learning. Specifically, we train a Neural Basis Expansion Analysis for Time Series (NBEATS) model on a vast dataset comprising diverse time series data. Then, the trained model is applied to forecast electric power generation using zero-shot learning. The results show that the proposed method achieves a lower error than the benchmark deep learning and statistical methods, especially in backtesting. Furthermore, the proposed method provides vastly superior execution time as it does not require problem-specific training.

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Multi-step ahead forecasting for electric power load using an ensemble model

Cited by 20 publications

References 32 publications

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Comparing the Simple to Complex Automatic Methods with the Ensemble Approach in Forecasting Electrical Time Series Data

Powering Electricity Forecasting with Transfer Learning

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