Short-Term Load Forecasting for Electrical Power Distribution Systems Using Enhanced Deep Neural Networks

The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution system for short-term load forecasting (STLF) originates from a thorough analysis of current trends, the emergence of the state-of-the-art use cases and approaches. STLF plays a crucial role in economic load dispa...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE access Ročník 12; s. 186856 - 186871
Hlavní autori:	Tsegaye, Shewit, Padmanaban, Sanjeevikumar, Tjernberg, Lina Bertling, Fante, Kinde Anlay
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Convolutional neural networks Data models Deep neural networks efficient and parallel genetic algorithm Electric power distribution Electric utilities EPGA enhanced LSTM Error analysis Forecasting Genetic algorithms load forecast Load forecasting Load history Load modeling Load shedding Long short term memory Neural networks Pattern analysis Peak load Power dispatch Predictive models Renewable energy sources Risk management Root-mean-square errors short-term load forecast Unit commitment Wind direction Wind speed
ISSN:	2169-3536, 2169-3536
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution system for short-term load forecasting (STLF) originates from a thorough analysis of current trends, the emergence of the state-of-the-art use cases and approaches. STLF plays a crucial role in economic load dispatch, hydrothermal coordination, system security assessment, load shedding, unit commitment, and cost-effective risk management in power systems with renewable energy sources. In this study, we introduce a Long Short-Term Memory (LSTM), augmented with enhancements inspired by an Efficient and Parallel Genetic Algorithm (EPGA) for the STLF of Jimma town power distribution system. To forecast the load in the short term, the model takes into account wind direction, wind speed, humidity, temperature, season, load history, and peak load due to holidays. The optimal linear combination of inputs for determining daily load is derived using EPGA and data from Ethiopian Electric Utility (EEU). The linearly combined data is then fed into the LSTM model for load prediction. During training, this allows the LSTM model to focus on the pattern of a single time-series data rather than the best combination of many input patterns. The proposed method uses the combination of EPGA and LSTM models for accurate STLF. Thorough experimental analysis indicates that the root-mean-squared error (RMSE) achieved by the EPGA-enhanced LSTM for Jimma town power distribution system STLF is about 43.87. This represents a 7.486% improvement over the prediction obtained using only LSTM model. Additionally, the mean average percentage error (MAPE) for five sample loads used to test the EPGA-enhanced LSTM prediction method further supports the robustness of the proposed method.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	2169-3536 2169-3536
DOI:	10.1109/ACCESS.2024.3432647