Short-term Electricity Load Forecasting for Thailand

Chapagain, Kamal

doi:10.1109/ecticon.2018.8619930

Cited by 7 publications

(11 citation statements)

References 10 publications

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“…Result Reference MLR with AR(2) Bayesian estimation provides consistent and better accuracy compared to OLS estimation [32] PSO with ANN Implementing PSO on ANN model outperformed shallow ANN model [46] OLS Interation of variable improves the prediction accuracy [31] OLS and Bayesian estimation Including temperature variable in a model can improved the prediction accuracy upto 20% [45] PSO & GA with ANN PSO+GA outperformed PSO with ANN [35] OLS, GLSAR, FF-ANN OLS and GLSAR models showed better forecasting accuracy than FF-ANN [36] Ensemble for regression and ML Lowers the test MAPE implementing blocked Cross Validation scheme. [37] FNN, RNN based LSTM & GRU For weekdays and for aggregate data GRU shows better accuracy In this study Weather conditions have a significant impact on short-term electricity demand forecasting and are commonly incorporated into forecasting models [43].…”

Section: Methodsmentioning

confidence: 99%

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Short-term Electricity Demand Forecasting using Deep Neural Networks: An analysis for Thai data

Chapagain¹,

Gurung²,

Kulthanavit³

2023

Preprint

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Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor to optimize power generation, consumption, saving energy resources, and determining the energy prices. However, integrating energy mix scenarios, including solar and wind power which are highly non-linear and seasonal, into an existing grid increases uncertainty in generation, adds the challenges for precise forecast. To tackle these challenges, state-of-the-art methods and algorithms have been implemented in literature. We have developed Artificial Intelligence (AI) based deep learning models that can effectively handle the information of long time-series data. Based on the pattern of dataset, four different scenarios were developed and two best scenarios were selected for prediction. Dozens of models were developed and tested in deep AI networks. In the first scenario (Scenario1), data for weekdays excluding holidays was taken and in the second scenario (Scenario2) all the data in the basket was taken. Remaining two scenarios, weekends and holidays were tested and neglected because of their high prediction error. To find the optimal configuration, models were trained and tested within a large space of alternatives called hyper-parameters. In this study, an Aritificial Neural Network (ANN) based Feed-forward Neural Network (FNN) showed the minimum prediction error for Scenario1 while a Recurrent Neural Network (RNN) based Gated Recurrent Network (GRU) showed the minimum prediction error for Scenario2. While comparing the accuracy, the lowest MAPE of 2.47% was obtained from FNN for Scenario1. When evaluating the same testing dataset (non-holidays) of Scenario2, the RNN-GRU model achieved the lowest MAPE of 2.71%. Therefore, we can conclude that grouping of weekdays as Senario1 prepared by excluding the holidays provides better forecasting accuracy compared to the single group approach used in Scenario2, where all the dataset is considered together. However, Scenario2 is equally important to predict the demand for weekends and holidays.

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Section: Methodsmentioning

confidence: 99%

“…• increasing the number of hidden layers does not ensure the improvement of forecasting accuracy. • as this dataset has been tested in various methods such as Bayesian [32], Regression [32,36,45], machine learning [33][34][35]48], ensemble learning [37] etc, the experimental gap on deep learning network is now fulfilled by this paper.…”

mentioning

confidence: 99%

Short-term Electricity Demand Forecasting using Deep Neural Networks: An analysis for Thai data

Chapagain¹,

Gurung²,

Kulthanavit³

2023

Preprint

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“…Bayesian estimation provides consistent and better accuracy compared to OLS estimation 1% to 5% [28] PSO with ANN Implementing PSO on ANN model outperformed shallow ANN model 3.44% [43] OLS Interation of variable improves the prediction accuracy >4% [44] OLS and Bayesian estimation Including temperature variable in a model can improved the prediction accuracy up to 20% 2% to 3% [29] PSO & GA with ANN PSO+GA outperformed PSO with ANN >3% [32] OLS, GLSAR, FNN OLS and GLSAR models showed better forecasting accuracy than FNN 1.74% to 2.95% [22] Ensemble for regression and ML Lowers the test MAPE implementing blocked Cross Validation scheme.…”

Section: Mlr With Ar(2)mentioning

confidence: 99%

“…The dataset we used has been tested in various contexts, such as Bayesian [28], regression [22,28,29], machine learning [30][31][32][33], and ensemble learning [27]; in this paper, we apply it in the context of deep learning networks.…”

mentioning

confidence: 99%

Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data

Chapagain,

Gurung,

Kulthanavit

et al. 2023

ASI

Self Cite

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Electricity demand forecasting plays a significant role in energy markets. Accurate prediction of electricity demand is the key factor in optimizing power generation and consumption, saving energy resources, and determining energy prices. However, integrating energy mix scenarios, including solar and wind power, which are highly nonlinear and seasonal, into an existing grid increases the uncertainty of generation, creating additional challenges for precise forecasting. To tackle such challenges, state-of-the-art methods and algorithms have been implemented in the literature. Artificial Intelligence (AI)-based deep learning models can effectively handle the information of long time-series data. Based on patterns identified in datasets, various scenarios can be developed. In this paper, several models were constructed and tested using deep AI networks in two different scenarios: Scenario1 used data for weekdays, excluding holidays, while Scenario2 used the data without exclusion. To find the optimal configuration, the models were trained and tested within a large space of alternative hyperparameters. We used an Artificial Neural Network (ANN)-based Feedforward Neural Network (FNN) to show the minimum prediction error for Scenario1 and a Recurrent Neural Network (RNN)-based Gated Recurrent Network (GRU) to show the minimum prediction error for Scenario2. From our results, it can be concluded that the weekday dataset in Scenario1 prepared by excluding weekends and holidays provides better forecasting accuracy compared to the holistic dataset approach used in Scenario2. However, Scenario2 is necessary for predicting the demand on weekends and holidays.

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“…Since short-term power loads are commonly used in daily or weekly scheduling plans, which have a guiding effect on the grid scheduling department, most domestic and international researchers are committed to improving the accuracy of shortterm power load forecasting. Traditional load forecasting methods include exponential smoothing, autoregressive integral sliding average model, multiple linear regression, and Weighted Moving Average (Chapagain and Kittipiyakul, 2018;Lee et al, 2018;Rosnalini et al, 2019;Nuo et al, 2023). Although these methods have good interpretability and fast computational speed, these methods are less robust and perform poorly in predicting large amounts of data and sudden power loads (Nuo et al, 2023).…”

mentioning

confidence: 99%

A short-term electricity load forecasting method integrating empirical modal decomposition with SAM-LSTM

Zhou,

Su,

Gao

et al. 2024

Front. Energy Res.

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Short-term power load forecasting is the basis for ensuring the safe and stable operation of the power system. However, because power load forecasting is affected by weather, economy, geography, and other factors, it has strong instability and nonlinearity, making it difficult to improve the accuracy of short-term power load forecasting. To solve the above problems, a load forecasting method combining empirical modal decomposition (EMD) and long short-term memory neural network (LSTM) has been proposed. The original signal is first decomposed into a series of eigenmode functions and a residual quantity using the EMD algorithm. Subsequently, all the components are fed into the LSTM network. To further improve the load prediction accuracy, a self-attention mechanism is introduced for large component signals to further explore the internal correlation of the data, and the Sparrow Optimisation Algorithm (SSA) is used to optimize the LSTM hyperparameters. Combining EMD, LSTM, self-attention mechanism (SAM), and SSA, the EMD-SSA- SAM -LSTM method for short-term power load forecasting is further proposed. The results show that the coefficient of determination (R2) of the method is 0.98, the mean absolute error (MAE) is 0.013, the root mean square error (RMSE) is 0.018, and the mean absolute percentage error (MAPE) is 2.57%, which verifies that the proposed model can improve the accuracy of load forecasting, and has a certain application prospect.

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Short-term Electricity Load Forecasting for Thailand

Cited by 7 publications

References 10 publications

Short-term Electricity Demand Forecasting using Deep Neural Networks: An analysis for Thai data

Short-term Electricity Demand Forecasting using Deep Neural Networks: An analysis for Thai data

Short-Term Electricity Demand Forecasting Using Deep Neural Networks: An Analysis for Thai Data

A short-term electricity load forecasting method integrating empirical modal decomposition with SAM-LSTM

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