Summary of Short-term Electricity Demand Forecasting Of Dhaka City Using Cnn with Stacked Bilstm, by Kazi Fuad Bin Akhter et al.
Short-Term Electricity Demand Forecasting of Dhaka City Using CNN with Stacked BiLSTM
by Kazi Fuad Bin Akhter, Sadia Mobasshira, Saief Nowaz Haque, Mahjub Alam Khan Hesham, Tanvir Ahmed
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a hybrid model combining Convolutional Neural Network (CNN) and stacked Bidirectional Long-short Term Memory (BiLSTM) architecture for short-term electricity demand forecasting in Dhaka city. This approach is crucial for ensuring a reliable and sustainable electricity supply in Bangladesh, a developing country with growing population and economy. The complex nonlinear behavior of energy systems hinders the creation of precise algorithms, making this hybrid model a promising solution. The proposed approach outperforms benchmark models (LSTM, CNN-BiLSTM, and CNN-LSTM) with MAPE 1.64%, MSE 0.015, RMSE 0.122, and MAE 0.092. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to predict how much electricity people will use in the future. This is important because it helps power companies plan for the right amount of energy to keep everyone connected. The method uses special computer programs called Convolutional Neural Network and Bidirectional Long-short Term Memory to look at patterns in past data and make predictions about what will happen in the next few hours or days. It’s like trying to guess what people might do based on what they’ve done before. The new way of predicting electricity use is more accurate than other methods, which could help power companies make better decisions. |
Keywords
» Artificial intelligence » Cnn » Lstm » Mae » Mse » Neural network
