Improvement of performance of short term electricity demand model with meteorological parameters

Chapagain, Kamal; Soto, Tomonori; Kittipiyakul, Somasak

doi:10.3126/kjem.v1i1.22016

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The demand for electrical energy is greatly influenced by the atmospheric conditions like temperature, humidity, wind speed etc., [ 60 , [73] , [74] , [75] ]. However, in the regions under consideration the influence of temperature is dominant compared to other weather parameters due to geographical conditions.…”

Section: Resultsmentioning

confidence: 99%

“…8 . Weather conditions like temperature, rainfall, humidity etc., and also type of days (week days and weekends) generally affects the consumer load demand [ 60 , 61 ]. Even though the load variation during pandemic period is mostly due to lockdown imposed and work from home policy adopted, [4] the temperature parameter is also considered for load prediction as it significantly effects residential power usage.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

Goel¹,

Assudani²,

Radhika³

et al. 2022

Electric Power Systems Research

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

Goel¹,

Assudani²,

Radhika³

et al. 2022

Electric Power Systems Research

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“…Weekend and weekday holidays both have significantly lower energy demand than weekday demand. In order that the various weekday patterns would be identified, we needed to create dummy variables that represented each day of a week as either weekdays, weekends, or holidays (whether on weekdays or weekends) [40]. Various researchers have generated noteworthy findings based on the identical EGAT dataset spanning from 2009 to 2013.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Shiwakoti,

Charoenlarpnopparut,

Chapagain

2024

Applied Sciences

Self Cite

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Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.

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“…In general, these factors are called deterministic variables. The demand on weekdays has significantly different patterns compared to the demand on weekends [19]. Specifically, weekday demand is considerably higher and more stable compared to weekend demand due to the presence of industrial sector operations on weekdays.…”

Section: Factors Affecting the Electric Energy Demandmentioning

confidence: 99%

Forecasting Short-Term Electricity Load Using Validated Ensemble Learning

Sankalpa

Laitrakun

2022

Energies

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As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such forecasting for Thai data. The proposed model uses voting regression (VR), producing forecasts with weighted averages of forecasts from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. The regressors are linear regression models with gradient-descent (LR), ordinary least-squares (OLS) estimators, and generalized least-squares auto-regression (GLSAR) models. In contrast, the machine-learning models are decision trees (DT) and random forests (RF). To select the best model variables and hyper-parameters, we used cross-validation (CV) performance instead of the test data performance, which yielded overly good test performance. We compared various validation schemes and found that the Blocked-CV scheme gives the validation error closest to the test error. Using Blocked-CV, the test results show that the VR model outperforms all its individual predictors.

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Improvement of performance of short term electricity demand model with meteorological parameters

Cited by 3 publications

References 16 publications

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Forecasting Short-Term Electricity Load Using Validated Ensemble Learning

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