The Effect of Driver Variables on the Estimation of Bi-variate Probability Density of Peak Loads for Robust Design of Multi-Energy Systems
It stands to reason that developing more accurate forecasting methods is the pillar of building robust multi-energy systems. In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, GDP, and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.
On 07 Jan, 2021
Received 19 Dec, 2020
On 19 Dec, 2020
Received 17 Dec, 2020
On 13 Dec, 2020
On 13 Dec, 2020
Invitations sent on 08 Dec, 2020
On 06 Dec, 2020
On 06 Dec, 2020
On 06 Dec, 2020
Posted 17 Aug, 2020
On 23 Sep, 2020
Received 11 Sep, 2020
Received 07 Sep, 2020
Received 07 Sep, 2020
On 28 Aug, 2020
On 28 Aug, 2020
On 28 Aug, 2020
Invitations sent on 27 Aug, 2020
On 27 Aug, 2020
On 14 Aug, 2020
On 13 Aug, 2020
On 13 Aug, 2020
On 31 Jul, 2020
The Effect of Driver Variables on the Estimation of Bi-variate Probability Density of Peak Loads for Robust Design of Multi-Energy Systems
On 07 Jan, 2021
Received 19 Dec, 2020
On 19 Dec, 2020
Received 17 Dec, 2020
On 13 Dec, 2020
On 13 Dec, 2020
Invitations sent on 08 Dec, 2020
On 06 Dec, 2020
On 06 Dec, 2020
On 06 Dec, 2020
Posted 17 Aug, 2020
On 23 Sep, 2020
Received 11 Sep, 2020
Received 07 Sep, 2020
Received 07 Sep, 2020
On 28 Aug, 2020
On 28 Aug, 2020
On 28 Aug, 2020
Invitations sent on 27 Aug, 2020
On 27 Aug, 2020
On 14 Aug, 2020
On 13 Aug, 2020
On 13 Aug, 2020
On 31 Jul, 2020
It stands to reason that developing more accurate forecasting methods is the pillar of building robust multi-energy systems. In this context, long-term forecasting is also indispensable to have a robust expansion planning program for modern power systems. While very short-term and short-term forecasting are usually represented with point estimation, this approach is highly unreliable in medium-term and long-term forecasting due to inherent uncertainty in predictors like weather variable in long terms. Accordingly, long-term forecasting is usually represented by probabilistic forecasting values which are based on probabilistic functions. In this paper, a self-organizing mixture network (SOMN) is developed to estimate the probability density function (PDF) of peak load in long-term horizons considering the most important drivers of seasonal similarity, population, GDP, and electricity price. The proposed methodology is applied to forecast the PDF of annual and seasonal peak load in Queensland Australia.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the latest manuscript can be downloaded and accessed as a PDF.