2019-Sustainable Industrial Processing Summit
SIPS2019 Volume 13: Composite, Ceramic, Nanomaterials, Polymers, and Mathematics
| Editors: | F. Kongoli, M. de Campos, S. Lewis, S. Miller, S. Thomas |
| Publisher: | Flogen Star OUTREACH |
| Publication Year: | 2019 |
| Pages: | 171 pages |
| ISBN: | 978-1-989820-12-4 |
| ISSN: | 2291-1227 (Metals and Materials Processing in a Clean Environment Series) |
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Modelling and Simulating Various Scenarios of Electricity Demand to Optimize the Cascade Production
Eralda Gjika1; Aurora Ferrja1; Lule Basha1; Arbesa Kamberi2;1UNIVERSITY OF TIRANA, Tirana, Albania; 2ALBANIAN POWER CORPORATION, Tirana, Albania;
Type of Paper: Regular
Id Paper: 357
Topic: 38
Abstract:
Forecasting energy production by hydropower plants (HPP) is a challenge because of their correlation with many exogenous variables such as precipitations, water inflow, temperature, the minimum and maximum level of the HPP, etc. Albania has a favorite geographical position which makes the electrical energy the main source of energy produced in the country. In our work, we try to analyze hourly and daily data of energy produced in the main cascade of the country which produces the main amount of energy consumption. Our focus is on analyzing the situation of energy demand on a 24 hour period and also on a weekly /monthly period. We analyze the seasonality patterns of the energy demand and try to fit different models to predict the upcoming season (hours or days). Several modeling strategies among hierarchical forecasting, neural network, multistage forecasting, econometric forecasting models were tested and the best was selected looking at the performance obtained on the testing period. Our goal is to use the proposed models to obtain the forecast for electric energy demand in the country which will help the Albanian Power Corporation (KESH) to build various scenarios on optimizing the country demand and production capacities on HPP cascade.
Keywords:
Mathematics;References:
[1] J. Campillo, F. Wallin , D. Torstensson , I. Vassileva Energy demand model design for forecasting electricity Consumption and simulating demand response scenario in Sweden, International Conference on Applied Energy ICAE 2012, Jul 5-8, 2012, Suzhou, China Paper ID: ICAE2012- A10599[2] J. Huang, Y. Tang, Sh. Chen Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm, Mathematical Problems in Engineering, Volume 2018, Article ID 5194810, 13 pages. https://doi.org/10.1155/2018/5194810
[3] Makridakis, S., et al., The M4 Competition: Results, findings, conclusion and way forward. International Journal of Forecasting (2018), https://doi.org/10.1016/j.ijforecast.2018.06.001.