Dynamic linear state space model for forecasting peak and short-term electricity demand using kalman filtered monte carlo method
| dc.contributor.author | Owusu, Frank Kofi | |
| dc.contributor.author | ||
| dc.contributor.author | ||
| dc.contributor.author | ||
| dc.date.accessioned | 2021-05-25T16:17:32Z | |
| dc.date.accessioned | 2023-04-19T03:04:27Z | |
| dc.date.available | 2021-05-25T16:17:32Z | |
| dc.date.available | 2023-04-19T03:04:27Z | |
| dc.date.issued | August, 2018 | |
| dc.description | A thesis submitted to the Department of Mathematics through The National Institute for Mathematical Sciences, Ghana, in partial fulfillment of the requirements for the award of Master of Philosophy degree (Scientific Computing and Industrial Modeling), | en_US |
| dc.description.abstract | Electricity has become a major part of human life, especially in our part of the world. It is one of the most used energy across the world. Due to the fast changing world, the demand for electricity keeps on increasing from time to time yet there is not any efficient way of storing this energy for future use. So operators are very cautious about the amount to release and also to meet the demand of their consumers. For this reason, load forecasting has become a main integrated section in energy manage ment and production. This research seeks to look at Short-Term Load Forecasting. The objective is to forecast the peak demand and total energy generated or elec tricity demand. So the Seemingly Unrelated Time Series Equations Model which models the level or state and trend in the system was used for the study. A Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter and Kalman Smoother, the Forward Filtering Backward Sampling with Gibbs Sampler Algorithm were applied to the model to simulate for the variances also to predict the peak demand the next day’s peak and electricity demand. The running ergodic mean showed the convergence of the MCMC process and hence the posterior means of the variances were estimated. The one-step-ahead forecast showed a Mean Absolute Percentage error (MAPE) of 3.696% error in the peak demand forecast and a 4.235% error in the electricity demand forecast. The forecast for the next day was about 2187MW and 44090MW for the peak and electricity demands respectively. For further studies, the model can be extended to capture seasonal components. | en_US |
| dc.description.sponsorship | KNUST | en_US |
| dc.identifier.uri | https://ir.knust.edu.gh/handle/123456789/13816 | |
| dc.language.iso | en_US | en_US |
| dc.subject | Forecasting peak | en_US |
| dc.subject | Electricity demand | en_US |
| dc.title | Dynamic linear state space model for forecasting peak and short-term electricity demand using kalman filtered monte carlo method | en_US |
| dc.type | Thesis | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- Frank Kofi Owusu_MPhil.pdf
- Size:
- 1.64 MB
- Format:
- Adobe Portable Document Format
- Description:
- Full Thesis