| dc.description.abstract |
In today's modern world, electricity plays a crucial role in everyday life, serving as an
essential resource for various activities. With uninterrupted access to electricity being a top
priority for nations worldwide, accurately forecasting electricity consumption has become
increasingly important. Such forecasts are instrumental in enhancing the reliability and
efficiency of power supply infrastructure. A review of the relevant studies shows that,
currently there is no application with which the electricity consumption of Dhaka city can
be predicted. In order to fill in the need for such an application, the primary objective of
this research was - conducting performance analysis among state of the art machine learning
algorithms in terms of predicting power. In order to accomplish this objective, consumption
data of a group of consumers from Dhaka city (specifically in Kafrul, Kallyanpur, Pallabi,
Monipur and Rupnagar area) was collected for a period of three years. Then the data was
used to train machine learning models for predicting power consumption with Artificial
Neural Network, Support Vector Machine, XGBoost, LightGBM and CatBoost. In these
models, features like monthly consumption (kilowatt-hour) for two years, sanctioned load,
meter type, etc. had been selected to predict the electricity consumption of next one year.
Grid search technique had been deployed to find the appropriate hyper-parameter values
for each algorithm. Afterwards, the performance of each of the proposed predictive models
was measured to determine their effectiveness by calculating MAE, RMSE, R2 values for
each algorithm. Experimental results present that Artificial Neural Network (ANN) and
Support Vector Machine (SVM) shows more accurate result compared to the other
algorithms as per MAE, RMSE, and R squared method. For example, the MAE for ANN
and SVM were 49.19 and 51.08 respectively, where the MAE for XGBoost, LightGBM
and CatBoost were 127.22, 165.62 and 70.81 respectively. However, in terms of time
required to train the model, LightGBM and CatBoost stands out among all these algorithms.
The secondary objective of this research was to develop a web application using the best
performing model to predict the electricity consumption of the consumers of Dhaka city.
To achieve this objective, a web application had been developed using the model trained
with Artificial Neural Network (ANN) where the users can put the electricity consumption
of any consumer during two years and predict the electricity consumption for the next one
year. Output of the experiment shows that adopting the methods may greatly benefit power generation, transmission and distribution entities as well as the government in forecasting
energy needs and plan accordingly. The methods used in this work can also be utilized to
reduce system loss and prevent equipment damage to a great extent. |
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