- Implementation a neural network-based prediction algorithm for accurately forecasting hourly energy consumption.
- Performed data preprocessing and cleaning on 12 Time-Series datasets.
- Conducted standard statistical analysis to describe and interpret the datasets.
- Utilized data visualization functions in Python to make clear and concise visual representations of datasets.
- Implemented Artificial Neural Netwok - regression based algorithm to train, predict, and compute prediction accuracy.
- Applied mlpRegressor in Skicit-learn and correctly predicted energy consumption for 12 different regions with an overall prediction score > 0.9
Siavash-H/E_Consumtion
Data cleaning, visualisation and predicting the model with ANN on Energy consumtion data
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