Short-Term Electricity Demand Forecasting with Seasonal and Interactions of Variables for Thailand

Chapagain, Kamal

doi:10.1109/ieecon.2018.8712189

Cited by 9 publications

(8 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, in most recent research, these models are less used for electricity STLF since ML methods provide better results, as demonstrated in References [23,24], and more recently in Reference [25]. Particularly, in this last cited study, the authors compare the performance of six classical data-driven regression models and two deep learning models to deliver a day-ahead forecast for Jiangsu province, China, concluding that the ARIMA model had several limitations to solve the STLF problem.…”

Section: Literature Reviewmentioning

confidence: 93%

“…The support vector regression (SVR) model is another popular model for STLF, mainly with a linear kernel, due to the linearity between the inputs and the forecast, as concluded by the authors of Reference [25], who obtained a MAPE under 2.6% for the day-ahead prediction, performing better than MLR and multivariate adaptive regression splines. Similarly, the authors of Reference [17] proposed forecasting the 48 h of Portuguese electricity consumption by using SVR as a better alternative after previously submitting the ANN's use for the same task in Reference [28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The error term is then constrained by a specified margin ε (epsilon). SVR is frequently used for STLF with the linear [25] or radial basis function (RBF) kernel [66], identifying load patterns better than other linear models [67].…”

Section: Modelingmentioning

confidence: 99%

See 2 more Smart Citations

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

Information

View full text Add to dashboard Cite

An accurate short-term load forecasting (STLF) is one of the most critical inputs for power plant units’ planning commitment. STLF reduces the overall planning uncertainty added by the intermittent production of renewable sources; thus, it helps to minimize the hydrothermal electricity production costs in a power grid. Although there is some research in the field and even several research applications, there is a continual need to improve forecasts. This research proposes a set of machine learning (ML) models to improve the accuracy of 168 h forecasts. The developed models employ features from multiple sources, such as historical load, weather, and holidays. Of the five ML models developed and tested in various load profile contexts, the Extreme Gradient Boosting Regressor (XGBoost) algorithm showed the best results, surpassing previous historical weekly predictions based on neural networks. Additionally, because XGBoost models are based on an ensemble of decision trees, it facilitated the model’s interpretation, which provided a relevant additional result, the features’ importance in the forecasting.

show abstract

Section: Literature Reviewmentioning

confidence: 93%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Electricity Load Forecasting with Machine Learning

Madrid

António

2021

Information

View full text Add to dashboard Cite

show abstract

“…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%

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

Chapagain¹,

Gurung²,

Kulthanavit³

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…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%

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

Chapagain,

Gurung,

Kulthanavit

et al. 2023

ASI

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Short-Term Electricity Demand Forecasting with Seasonal and Interactions of Variables for Thailand

Cited by 9 publications

References 7 publications

Short-Term Electricity Load Forecasting with Machine Learning

Short-Term Electricity Load Forecasting with Machine Learning

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

Contact Info

Product

Resources

About