kWh Demand Assessment and Forecasting of a Low Voltage Private Academic Institution
Joy R. Antonio1, Antonio S. Buendia Jr.2, Gina B. Narca3, Noel T. Florencondia4, Lorinda Pascua5
1Joy R. Antonio*, Degree, Nueva Ecija University of Science and Technology, Philippines.
2Antonio S. Buendia Jr., Pursued Electrical Engineering, Asia-the Nueva Ecija University of Science and Technology, Philippines.
3Gina B. Narca, Professional Philippine Rice Research Institute, Central Experiment Station located at Maligaya, Science City of Munoz, Nueva Ecija, Philippines.
4Noel T. Florencondia, Dean, College of Engineering, Nueva Ecija University of Science and Technology,
5Lorinda Pascual, Professor, Nueva University of Science and Technology. Philippines.
Manuscript received on January 12, 2021. | Revised Manuscript received on April 10, 2021. | Manuscript published on April 30, 2021. | PP: 152-157 | Volume-10 Issue-4, April 2021. | Retrieval Number: 100.1/ijeat.C21580210321 | DOI: 10.35940/ijeat.C2158.0410421
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Transformer is one of the crucial components in an electrical system. They are used to convert energy to a utilization level. Load allocation is vital to sustaining the needed energy demand for a growing population, and it is an essential consideration for continuous consumer connectivity of electricity. In this study, the researchers are conducting assessment and forecast of the transformer requirement for a Low Voltage Private Academic Institution. It assessed the kWh demands and kWh allocation from 2005 to 2019 of the three transformers used of the locale. The kWh demand was used to forecast the per semiannual for the next five years. Six forecasted models were applied and the result were verified by QM for Windows Version 5. Mean Absolute Error (MAE), Mean Square Error (MSE) and Mean Absolute Percent Error (MAPE) were used to select the best forecasting model with the least value. DT (Distribution Transformer) 1 used the 4th degree polynomial model because of the least value of MAE, MSE and MAPE of 5340.626, 42306100 and 17.12%, while DT 2 and DT 3 applied the 3 rd degree polynomial model though it has the second lowest value of MAE, MSE and MAPE of 3142.907, 16748570, 20.44% and 2576.398, 8740315, 11.21% respectively because the projected demand of the 4 th degree gave a negative value.. The researcher finds that DT 1 and 3 were starting to exceeds the allotted kWh. On the other hand, DT 2 and 4 were plenty of kWh allocation. Relocation of load from DT 1 and 3 to DT 2 may take for considerations by the institution.
Keywords: Distribution Transformer), kWh Demand, kWh allocation, Forecasting.
Scope of the Article: Distribution Transformer