Electricity Consumption in Higher Education Buildings in Thailand during the COVID-19 Pandemic

Chaloeytoy, Kittiwoot; Inkarojrit, Vorapat; Thanachareonkit, Anothai

doi:10.3390/buildings12101532

Cited by 10 publications

(3 citation statements)

References 51 publications

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“…During that time many factories, universities, and offices in Bangkok and surrounding provinces were closed. Figure 5a indicates that the measure of the demand drop compared with respect to the previous year's demand shows that the peak demand was reduced approximately by 2000 MW, and can be considered similar to the COVID-19 pandemic lockdown [22,51]. Each year, there is a noticeable decrease in electricity demand during holidays and special events.…”

Section: Seasonal and Holiday Patternmentioning

confidence: 86%

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

Chapagain,

Gurung,

Kulthanavit

et al. 2023

ASI

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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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Section: Seasonal and Holiday Patternmentioning

confidence: 86%

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

Chapagain,

Gurung,

Kulthanavit

et al. 2023

ASI

View full text Add to dashboard Cite

show abstract

“…In other study, Chaloeytoy et al (2022) found that switching to the online learning mode significantly decreased electricity consumption in higher education facilities in Thailand. Samuels et al (2021) also found that the impact of COVID-19 on the EU of schools in South Africa results show a substantial reduction ranging between 30% and 40% during the hard lockdown.…”

Section: Introductionmentioning

confidence: 85%

Research on air conditioning energy use and indoor thermal environment with Private Finance Initiative data monitoring of junior high schools before and during the COVID-19 pandemic in Japan

et al. 2023

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“…During that time many factories, universities, offices in Bangkok and surrounding provinces were closed for a few months. Figure 5a indicates that the measure of the demand drop compared with respect to previous year's demand, shows that the peak demand was reduced approximately by 2000MW and can be considered the similar stage of Covid-19 lockdown situation [36,56]. Each year, there is a noticeable decrease in electricity demand during holidays and special events.…”

Section: Seasonal and Holiday Patternmentioning

confidence: 92%

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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Electricity Consumption in Higher Education Buildings in Thailand during the COVID-19 Pandemic

Cited by 10 publications

References 51 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

Research on air conditioning energy use and indoor thermal environment with Private Finance Initiative data monitoring of junior high schools before and during the COVID-19 pandemic in Japan

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

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