Short Term Load Forecasting Based on SBiGRU and CEEMDAN-SBiGRU Combined Model

Tian, Chen; Huang, Wei; Wu, Rujun; Hua-bing, Ouyang

doi:10.1109/access.2020.3043043

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…In K-Fold CV, we further split our training set into K number of subsets, called folds. We then iteratively fit the model K times, each time training the data on K-1 of the folds and evaluating on the Kth fold (called the validation data) [15]. As an example, consider fitting a model with K = 5.…”

Section: • Cross Validationmentioning

confidence: 99%

Appliances Energy Prediction using Supervised Machine Learning Approach

Beldar

2024

Preprint

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This paper presents and discusses data-driven predictive models for the energy use of appliances. Data used include measurements of temperature and humidity sensors from a wireless network, weather from a nearby airport station and recorded energy use of lighting fixtures. The paper discusses data filtering to remove non-predictive parameters and feature ranking. When using all the predictors. From the wireless network, the data from the kitchen, laundry and living room were ranked the highest in importance for the energy prediction. The prediction models with only the weather data, selected the atmospheric pressure (which is correlated to wind speed) as the most relevant weather data variable in the prediction. Therefore, atmospheric pressure may be important to include in energy prediction models and for building performance modelling.

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Section: • Cross Validationmentioning

confidence: 99%

Appliances Energy Prediction using Supervised Machine Learning Approach

Beldar

2024

Preprint

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“…Boxplot is a standardized way for displaying the distribution of data by dividing the dataset into equal fourths, as shown in Fig. 2 [23][24][25][26]. The 1 st (Q1), the 2 nd (Q2) and the 3 rd (Q3) quartiles correspond to the 25 th , 50 th , and 75 th percentile of the dataset.…”

Section: Median Grid Search Algorithmmentioning

confidence: 99%

A New Grid Search Algorithm Based on Median Values for SVR Model in Case of Load Forecasting

Tran

2023

Period. Polytech. Elec. Eng. Comp. Sci.

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In this paper, a Median Grid Search algorithm based on the median values is proposed to develop a more accurate algorithm used to investigate the optimal hyperparameter of the SVR model for load forecasting. In addition, the methodology to benchmark the proposed Grid Search and the conventional Grid Search is built and sufficiently utilized. The data gathered from the South Australia state, Australia and Ho Chi Minh City load demands are used in experiments. Experimental results demonstrate that the proposed algorithm outperforms the conventional algorithm.

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“…2.1 Complete ensemble empirical mode decomposition with adaptive noise process, which not only mitigates the inherent mode-mixing phenomenon of the existing EMD but also considerably reduces the signal reconstruction error, making the reconstructed signal almost identical to the original one (Chen et al, 2021).…”

Section: Decomposition Of the Load Time Seriesmentioning

confidence: 99%

CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection

et al. 2023

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The rapidly increasing randomness and volatility of electrical power loads urge computationally efficient and accurate short-term load forecasting methods for ensuring the operational efficiency and reliability of the power system. Focusing on the non-stationary and non-linear characteristics of load curves that could easily compromise the forecasting accuracy, this paper proposes a complete ensemble empirical mode decomposition with adaptive noise–CatBoost–self-attention mechanism-integrated temporal convolutional network (CEEMDAN-CatBoost-SATCN)-based short-term load forecasting method, integrating time series decomposition and feature selection. CEEMDAN decomposes the original load into some periodically fluctuating components with different frequencies. With their fluctuation patterns being evaluated with permutation entropy, these components with close fluctuation patterns are further merged to improve computational efficiency. Thereafter, a CatBoost-based recursive feature elimination algorithm is applied to obtain the optimal feature subsets to the merged components based on feature importance, which can effectively reduce the dimension of input variables. On this basis, SATCN which consists of a convolutional neural network and self-attention mechanism is proposed. The case study shows that time series decomposition and feature selection have a positive effect on improving forecasting accuracy. Compared with other forecasting methods and evaluated with a mean absolute percentage error and root mean square error, the proposed method outperforms in forecasting accuracy.

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Short Term Load Forecasting Based on SBiGRU and CEEMDAN-SBiGRU Combined Model

Cited by 15 publications

References 26 publications

Appliances Energy Prediction using Supervised Machine Learning Approach

Appliances Energy Prediction using Supervised Machine Learning Approach

A New Grid Search Algorithm Based on Median Values for SVR Model in Case of Load Forecasting

CEEMDAN-CatBoost-SATCN-based short-term load forecasting model considering time series decomposition and feature selection

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