Abstract
Load forecasting in an emerging smart grid has become a challenging task. This paper presents an innovative approach to forecast highly variable smart customer load using smart meter energy consumption data. The smart meter data is systematically linearized by applying extended k-means clustering approach, smoothing the linearized load profiles and then linearizing the load profiles using Taylor series linearization process. Case studies are presented using real world smart meter data and then applying the proposed approach and artificial neural network. Four different scenarios are considered for forecasting and the results showed that, in case of high variability in smart customer energy demand, the accuracy of forecasting using linearized profiles is higher than using original non-linear profiles as the source of forecasting. The forecasting process was repeated several times to verify the robustness of the approach and the results justify the accuracy of the forecast further with the proposed approach.
| Original language | English |
|---|---|
| Title of host publication | 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG) |
| Publisher | IEEE Xplore |
| Number of pages | 5 |
| ISBN (Electronic) | 9781538654828 |
| ISBN (Print) | 9781538654835 (PoD) |
| DOIs | |
| Publication status | Published - 14 Jun 2018 |
| Event | International Conference on Power, Energy and Smart Grid - Mirpur University of Science and Technology (MUST), Pakistan Duration: 9 Apr 2018 → 10 Apr 2018 http://conference.must.edu.pk/ |
Conference
| Conference | International Conference on Power, Energy and Smart Grid |
|---|---|
| Abbreviated title | ICPESG-2018 |
| Country/Territory | Pakistan |
| Period | 9/04/18 → 10/04/18 |
| Internet address |
Keywords
- Artificial neural network
- Load forecasting
- Smart grid
- Smart meter
Access to Document
- Khan_&_Jayaweera_Approach_for_Forecasting_Smart_Customer_Demand_2018
Checked for eligibility: 04/06/2018 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works Khan, Z.A. and Jayaweera, D., 2018, April. Approach for forecasting smart customer demand with significant energy demand variability. In 2018 1st International Conference on Power, Energy and Smart Grid (ICPESG) (pp. 1-5). IEEE.