This thesis is a part of the master's in data science course at LTU. The core objective would be to build models that can do a short-term prediction of electricity energy consumption based on historical consumption data. With the increasing demand for electricity, forecasting electricity consumption is important and must be more accurate and closer to the actual values. As a part of this thesis, three different time series forecasting models are studied and experimented. The first model is based on an ensemble of Facebook prophet and XGBoost models together, the second model is based on deep learning neural network using Long short-term memory a Recurrent Neural Network, the third model is based on Convolution neural network. The performance of these three models is discussed and improvements needed, are also mentioned.These three models are trained with data from 2014-2019 and predictions are evaluated with 2020. As 2020 is the core of the COVID-19 pandemic season, offices were closed and this has impact on the model performance and evaluations. These impacts are also highlighted. Cross Industry standard process for Data mining methodology is followed in this thesis.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-94914 |
| Date | January 2022 |
| Creators | Movva, Venkata Sreenadh |
| Publisher | Luleå tekniska universitet, Institutionen för system- och rymdteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.002 seconds