Short-Term Electrical Load Forecasting DeepAR is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Short-Term Electrical Load Forecasting DeepAR Project Details
| Abstract |
This project looks at a new way to forecast short‑term electricity demand in different types of users – commercial, residential, and industrial. It uses the DeepAR algorithm, which is based on a Recurrent Neural Network (RNN) with Long Short‑Term Memory (LSTM) units. Instead of giving a single number, the model predicts a probability distribution for the load. The approach goes beyond the usual single‑area forecasts. It studies how demand in one region affects another and measures how these cross‑regional links change the forecast accuracy. By calculating correlation coefficients from past consumption data, the study quantifies the interaction between different consumer groups. Simulations show that adding these cross‑regional relationships lets DeepAR
capture the fast, non‑linear changes in electricity use more accurately. As a result, grid operators get reliable decision‑support numbers that help them design targeted, efficient, and adaptable power‑supply plans. The project also provides detailed instructions on cleaning the data, building the model, tuning hyperparameters, and comparing the results with traditional forecasting methods.
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| Reference Paper |
Short-Term Electrical Load Forecasting Based on the DeepAR Algorithm and Industry-Specific Electricity Consumption Characteristics |
| Domain |
Electrical Engineering |
| Sub-Domain |
Power Systems / Load Forecasting |
| PDF Download |
View Source |
| Get Help |
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