Demand-side load forecasting in smart grids using machine learning techniques
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View abstract on PubMed
Summary
This summary is machine-generated.Accurate electrical load forecasting is improved with a novel three-tier architecture and advanced weather data utilization. Support vector regression demonstrated superior performance in predicting energy consumption.
Area Of Science
- Electrical Engineering
- Data Science
- Artificial Intelligence
Background
- Electrical load forecasting faces challenges due to dynamic environmental factors like weather.
- Traditional methods struggle with Big Data management from IoT devices and smart meters.
- Existing two-tier architectures often suffer from low precision and overfitting.
Purpose Of The Study
- To develop an advanced, robust electrical load forecasting system.
- To leverage Big Data and IoT for improved energy consumption prediction.
- To enhance forecasting accuracy by incorporating underutilized weather features.
Main Methods
- A two-level forecasting approach using Daily Consumption Electrical Networks (DCEN) and Intra Load Forecasting Networks (ILFN).
- Implementation of a three-tier architecture: cloud, fog, and edge layers.
- Utilizing conventional neural networks to mitigate overfitting and employing Support Vector Regression (SVR).
Main Results
- Support Vector Regression outperformed other methods in experimental evaluations.
- Achieved a Mean Absolute Percentage Error (MAPE) of 5.055.
- Obtained a Root Mean Square Error (RMSE) of 0.69 and R2 score of 0.86.
Conclusions
- The proposed three-tier architecture and enhanced feature set significantly improve electrical load forecasting.
- The study validates the effectiveness of Support Vector Regression for this task.
- The findings demonstrate a more accurate and reliable approach to energy consumption prediction.
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