Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview
Abstract
:1. Introduction
2. STLF Methodologies
2.1. SimilarPattern Method
2.2. Variable Selection Method
2.3. Hierarchical Forecasting
2.4. Weather Station Selection
3. Method Evaluation and Future Work
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Nomenclature
ANN  Artificial Neural Network 
ARIMA  AutoRegressive Integrated Moving Average 
ARMA  AutoRegressive Moving Average 
CI  Computational Intelligence 
DTW  Dynamic Time Warping 
GEFC  Global Energy Forecasting Competition 
HSTLF  Hierarchical ShortTerm Load Forecasting 
STLF  ShortTerm Load Forecasting 
MI  Mutual Information 
RNN  Recurrent Neural Network 
SARIMA  Seasonal AutoRegressive Integrated Moving Average 
SVM  Support Vector Machine 
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Paper  Method  Formulae  Parameters 

[21]  Euclidean distance minimization  $\underset{i}{\mathrm{min}}{\sum}_{t=1}^{24}\left(w{\left(t\right)}^{f}w{\left(t\right)}^{i}\right),i\in \theta $ (1)  θ: historical days f: forecast day i: historical day in θ $w$: weather factor under consideration 
[12,22]  Weighted Euclidean distance minimization  $\underset{t}{\mathrm{min}}\sqrt{{w}_{1}{\left(\Delta {L}_{t}\right)}^{2}+{w}_{2}{\left(\Delta {L}_{s}\right)}^{2}+{w}_{3}{\left(\Delta {T}_{t}\right)}^{2}}$ (2)  $\Delta {L}_{t}$: deviation of load of forecast day and historical day $\Delta {L}_{s}$: deviation of slope between load on forecast day and load of historical day $\Delta {T}_{t}$: deviation of temperature between forecast day and historical day ${w}_{n}$: Weight factor 
Method  Publications  Technique 

SimilarPattern Method  
[12,20,21,22,23,35,36]  similar day  
[24,25,26,27,37,38,39,40,41]  patternsequence  
[28,29,30,31,32,33,34]  sequence learning 
Publication  Technique 

[11,48,49]  Stepwise 
[57,58]  Filter 
[3,9,47,51,65,66]  Correlation 
[47,53,54,55,67]  Mutual Information 
[60,61,62,63,64,68,69]  Optimization Algorithms 
Combination Method  Formulae  Parameters 

Linear Programming  $w=\mathrm{arg}\text{}\underset{\mathrm{w}}{\mathrm{min}}{\displaystyle {\displaystyle \sum}_{t=1}^{T}}\frac{1}{T}\frac{\left\widehat{Y}\tilde{Y}\right}{\widehat{Y}}$ $\tilde{Y}={\displaystyle {\displaystyle \sum}_{n=1}^{N}}{w}_{n}\text{}{\tilde{Y}}_{n}$  $\widehat{Y}$: base forecast $\tilde{Y}$: adjusted forecast $w$: weight factor 
Quadratic Programming  $\underset{\tilde{Y}}{\mathrm{min}}\frac{1}{2}{(\widehat{Y}\tilde{Y})}^{T}{\Sigma}^{1}(\widehat{Y}\tilde{Y})$ $\tilde{Y}={\left[{\tilde{a}}^{T},{\tilde{b}}^{T}\right]}^{T}$ $\tilde{a}=p\text{}\tilde{b}$  $\widehat{Y}$: base forecast $\tilde{Y}$: adjusted forecast $\tilde{a}$: load of the aggregated level $\tilde{b}$: Load of the disaggregated level $p$: participation factor 
Method  Advantages  Disadvantages 

SimilarPattern Method 


Variable Selection Method 


Hierarchical Method 


Weather Station Selection 


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Fallah, S.N.; Ganjkhani, M.; Shamshirband, S.; Chau, K.w. Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview. Energies 2019, 12, 393. https://doi.org/10.3390/en12030393
Fallah SN, Ganjkhani M, Shamshirband S, Chau Kw. Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview. Energies. 2019; 12(3):393. https://doi.org/10.3390/en12030393
Chicago/Turabian StyleFallah, Seyedeh Narjes, Mehdi Ganjkhani, Shahaboddin Shamshirband, and Kwokwing Chau. 2019. "Computational Intelligence on ShortTerm Load Forecasting: A Methodological Overview" Energies 12, no. 3: 393. https://doi.org/10.3390/en12030393