A Modified Feature Selection and Artificial Neural NetworkBased DayAhead Load Forecasting Model for a Smart Grid
Abstract
:1. Introduction
2. Related Work
3. Our Proposed Work
3.1. PreProcessing Module
 Local maximum: Initially, a local maximum value is calculated for each column of the P matrix; ${p}_{max}^{{c}_{i}}=max\{{p}_{{h}_{i}}^{{d}_{1}},{p}_{{h}_{i}}^{{d}_{2}},{p}_{{h}_{i}}^{{d}_{3}},\dots ,{p}_{{h}_{i}}^{{d}_{n}}\}$, $\forall \phantom{\rule{0.277778em}{0ex}}i\in \{1,2,3,\dots ,n\}$.
 Local normalization: In this step, each column of the matrix P is normalized by its respective local maxima, such that the resultant matrix is represented by ${P}_{nrm}$. Now, each entry of ${P}_{nrm}$ ranges between zero and one.
 Local median: For each column of the ${P}_{nrm}$ matrix, a local median value $Me{d}_{i}$ is calculated ($\forall \phantom{\rule{0.277778em}{0ex}}i\in \{1,2,3,\dots ,n\}$).
 Binary encoding: Each entry of the ${P}_{nrm}$ matrix is compared to its respective $Me{d}_{i}$ value. If the entry is less than its respective local median value, then it is encoded with a binary zero; else, it is encoded with a binary one. In this way, a resultant matrix containing only binary values (zeroes and ones), ${P}_{b}$, is obtained.
3.2. Feature Selection Module
3.3. Forecast Module
Algorithm 1 Dayahead load forecast. 

4. Simulation Results
 Error performance: This is the difference between the actual and the forecast signal/curve and is measured in %.
 Convergence rate or execution time: This is the simulation time taken by the system to execute a specific forecast model. Forecast models for which the execution time is small are said to converge quickly as compared to the opposite case. In this paper, execution time is measured in seconds.
Parameter  Value 

Number of forecasters  24 
Number of hidden layers  1 
Number of neurons in the hidden unit  5 
Number of iterations  100 
Momentum  0 
Initial weights  $0.1$ 
Historical load data  26 days 
Bias value  0 
5. Conclusion and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
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Ahmad, A.; Javaid, N.; Alrajeh, N.; Khan, Z.A.; Qasim, U.; Khan, A. A Modified Feature Selection and Artificial Neural NetworkBased DayAhead Load Forecasting Model for a Smart Grid. Appl. Sci. 2015, 5, 17561772. https://doi.org/10.3390/app5041756
Ahmad A, Javaid N, Alrajeh N, Khan ZA, Qasim U, Khan A. A Modified Feature Selection and Artificial Neural NetworkBased DayAhead Load Forecasting Model for a Smart Grid. Applied Sciences. 2015; 5(4):17561772. https://doi.org/10.3390/app5041756
Chicago/Turabian StyleAhmad, Ashfaq, Nadeem Javaid, Nabil Alrajeh, Zahoor Ali Khan, Umar Qasim, and Abid Khan. 2015. "A Modified Feature Selection and Artificial Neural NetworkBased DayAhead Load Forecasting Model for a Smart Grid" Applied Sciences 5, no. 4: 17561772. https://doi.org/10.3390/app5041756