Model Predictive Control Based Energy Flow Control in the Smart Grid

Hesselmann, Frederik; Zhu, Caroline Charlotte; Köppen-Seliger, Birgit; Ding, Steven X.

doi:10.1007/978-3-030-85318-1_39

Cited by 1 publication

(1 citation statement)

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“…The researchers explored model predictive control for adjusting power production and distribution strategies in response to predicted demand, thus preventing delays and optimizing resource use. By precisely predicting short-term load demand, services can enhance grid stability, reduce costs, and boost efficiency [33,34].…”

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

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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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Section: Related Workmentioning

confidence: 99%