# Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

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## Abstract

**:**

## 1. Introduction

## 2. Methodology

- Pre-processing stage
- Learning stage
- Performance evaluation stage

## 3. Datasets Used in CI Approaches for ILF

## 4. State-Of-The-Art of Single and Hybrid CI Techniques Applied for ILF

#### 4.1. Single (Stand-Alone Modeling) Methods

#### 4.1.1. Fuzzy Logic Sets

#### 4.1.2. Artificial Neural Networks (ANN)

#### 4.1.3. Support Vector Machine (SVM)

#### 4.1.4. Clustering Techniques

#### 4.1.5. Genetic Algorithm (GA)

#### 4.1.6. Artificial Bee Algorithm

#### 4.1.7. Artificial Immune System (AIS)

#### 4.1.8. Particle Swarm Optimization (PSO)

#### 4.2. Hybrid Methods

#### 4.2.1. Neuro-Fuzzy (NF)

#### 4.2.2. Artificial Neural Network and Wavelet Transform

#### 4.2.3. Optimization Algorithms Integrated with Artificial Neural Network

#### Artificial Neural Network and Genetic Algorithm

**.**

#### Artificial Neural Network and Fruit Fly Optimization Algorithm (ANN-FOA)

#### Artificial Neural Network and Firefly Algorithm (ANN-FA)

#### Artificial Neural Network and Artificial Immune Systems (ANN-AIS)

#### Artificial Neural Network and Particle Swarm Optimization (ANN-PSO)

#### 4.2.4. Artificial Neural Network and Clustering Techniques

#### 4.2.5. Optimization Algorithms Integrated with Support Vector Machine

#### Genetic Algorithm and Support Vector Machine

#### Support Vector Machine and Simulated Annealing Algorithm (SVM-SA)

#### Support Vector Machine and Particle Swarm Optimization (SVM-PSO)

#### Support Vector Machine and Artificial Bee Colony (SVM-ABC)

#### Support Vector Machine and Harmony Search Algorithm (SVM-HS)

#### Support Vector Machine and Fruit Fly Optimization Algorithm (SVM-FOA)

#### Support Vector Regression and Firefly Algorithm (SVR-FA)

## 5. Criteria Used for Evaluation

^{2}, which measures how the model predicts the trend of actual values. As it can be seen in the table, R

^{2}is directly related to RMSE.

^{2}) to assess the qualification of the proposed method. The results for R

^{2}value were equal to 0.9985.

## 6. Method Evaluation

## 7. Conclusions

## Author Contributions

## Conflicts of Interest

## Nomenclature

ABC | Artificial bee colony |

AIS | Artificial immune system |

ANN | Artificial neural network |

CI | Computational intelligence |

DE | Differential evolutionary algorithm |

DL | Deep learning |

ELM | Extreme learning machine |

FA | Firefly algorithm |

FANN | Firefly neural network |

FCM | Fuzzy C-means |

FL | Fuzzy logic |

FOA | Fruit fly optimization algorithm |

FRB | Fuzzy rule base |

GA | Genetic algorithm |

GNN | Genetic neural network |

GP | Genetic programming |

GSVM | Genetic support vector machine |

HS | Harmony search algorithm |

ILF | Intelligent load forecasting |

LTLF | Long-term load forecasting |

MLP | Multilayer perceptron |

MTLF | Medium-term load forecasting |

NF | Neuro fuzzy |

NILM | Non-Intrusive Load Management |

RNNs | recurrent neural networks |

WT | Wavelet transform |

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**Figure 4.**Comparison of MAPE values of hybrid algorithms developed by [39].

**Figure 5.**Comparison of MAPE values of hybrid algorithms developed by [46].

**Figure 6.**MAPE values of several hybrid and single algorithms developed for ILF by [58].

**Figure 7.**MAPE values of several SVM hybrid algorithms and ANN developed for ILF in [54].

**Figure 8.**MAPE values of several Hybrid SVM and single ANN algorithms developed for ILF in [100].

Type Classifier | Author | Title of Paper | Objectives |
---|---|---|---|

Single Methods | |||

FCM | Zhu et al. [16] | Short-term Load Forecasting Model Using Fuzzy C Means Based Radial Basis Function Network | To present application of Fuzzy C-mean to STLF. |

FRB | Welikala et al. [17] | Incorporating Appliance Usage Patterns for Non-Intrusive Load Monitoring and Load Forecasting | To develop a load monitoring method that can predict the amount of flexible load available at consumer premises. |

Khosravi et al. [18] | Interval Type-2 Fuzzy Logic Systems for Load Forecasting: A Comparative Study | To examine the application of FRB for STLF. | |

MLP | Ferreira et al. [19] | Toward Estimating Autonomous Neural Network-Based Electric Load Forecasters | To apply NN-based learning technique for STLF. |

Ding et al. [20] | Neural Network-Based Model Design for Short-Term Load Forecast in Distribution Systems | To design an ILF model using MLP arrangement. | |

Kong et al. [21] | Short-Term Residential Load Forecasting based on LSTM Recurrent Neural Network | To develop a short-term residential load forecasting model. | |

SOM | Lopez et al. [22] | Application of SOM Neural Networks to Short-Term Load Forecasting: The Spanish Electricity Market Case Study | Presents a forecasting model based on SOM algorithm. |

Llanos et al. [23] | Load Estimation for Microgrid Planning Based on a Self-Organizing Map Methodology | To apply SOM algorithm as a clustering technique for load forecasting in a micro grid. | |

DL | Shi et al. [24] | Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN | Exploring the capability of DL algorithm to directly learn uncertainties in load databases. |

Kong et al. [25] | Short-Term Residential Load Forecasting based on Resident Behavior Learning | Developing an ILF model utilizing appliance load data. | |

ELM | Ertugrul [26] | Forecasting Electricity Load by a Novel Recurrent Extreme Learning Machines Approach | Presenting an ILF model based on ELM. |

SAMI EKICI [27] | Electric Load Forecasting Using Regularized Extreme Learning Machines | Investigating the performances of ELM for ILF. | |

SVM | Chen et al. [28] | Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 | To forecast the maximum values of daily load demand. |

Elattar et al. [29] | Electric Load Forecasting Based on Locally Weighted Support Vector Regression | Modifying SVR algorithm to solve the ILF problem. | |

Clustering techniques | Alvarez et al. [30] | Energy Time Series Forecasting Based on Pattern Sequence Similarity | To predict the load time series using k-shape clustering algorithm to find the similarity of pattern sequences. |

ABC | Safamehr et al. [31] | A Cost-Efficient and Reliable Energy Management of a Micro-Grid Using Intelligent Demand–Response Program | Developing ABC algorithm to reshape the load profile by reducing the demand peak. |

AIS | Dudek [13] | Artificial Immune System with Local Feature Selection for Short-Term Load Forecasting | To design an AIS algorithm for STLF. |

PSO | AlRashidi et al. [32] | Long-Term Electric Load Forecasting Based on Particle Swarm Optimization | Presents a new method for long-term ILF. |

GP | Lee et al. [33] | Genetic programming model for long-term forecasting of electric power demand | To discuss the application of GP to solve ILF. |

EA | Logenthiran et al. [2] | Demand-Side Management in Smart Grid Using Heuristic Optimization | Proposing a technique to shift the day ahead peak load. |

Hybrid Methods | |||

Neuro-fuzzy | Yun et al. [34] | RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment | To investigate the feasibility of a neuro-fuzzy based load forecasting model in a real-time price environment. |

Chaouachi et al. [35] | Multi-Objective Intelligent Energy Management for a Microgrid | Proposed an optimization model to balance the supply-demand in a microgrid. | |

WT-NN | Li et al. [36] | Short-Term Load Forecasting by Wavelet Transform and Evolutionary Extreme Learning Machine | Proposes a novel STLF method. |

Guan et al. [37] | Very Short-Term Load Forecasting: Wavelet Neural Networks With Data Pre-Filtering | Develop an ANN algorithm with data pre-filtering for ILF one hour ahead. | |

GNN | Ling et al. [38] | A Novel Genetic-Algorithm-Based Neural Network for Short-Term Load Forecasting | To propose a GA-based neural network model for STLF. |

Azadeh et al. [14] | Integration of Artificial Neural Networks and Genetic Algorithm to Predict Electrical Energy Consumption | Evaluation the application of GA-ANN for ILF. | |

FOA_NN | Li et al. [39] | A Hybrid Annual Power Load Forecasting Model Based on Generalized Regression Neural Network with Fruit Fly Optimization Algorithm | To develop a hybrid annual load forecasting model. |

Rui Hu et al. [40] | A Short-Term Power Load Forecasting Model Based on the Generalized Regression Neural Network with Decreasing Step Fruit Fly Optimization Algorithm | Proposed a short-term power load forecasting model based on the ANN, optimized by FOA. | |

FA_NN | Liye Xiao et al. [41] | A Combined Model Based on Multiple Seasonal Patterns and Modified Firefly Algorithm for Electrical Load Forecasting | Improving the forecasting accuracy using combined neural network with FA. |

AIS-NN | Yong [42] | Short-Term Load Forecasting Using Artificial Immune Network | Presenting a method for STLF in power system. |

Mishra et al. [43] | Short-Term Load Forecasting Using a Neural Network Trained by a Hybrid Artificial Immune System | To propose a hybrid AIS algorithm for short-term load prediction. | |

PSO-NN | Nian Liu et al. [44] | A Hybrid Forecasting Model with Parameter Optimization for Short-Term Load Forecasting of Micro-Grids | To propose a hybrid model with parameter optimization for ILF in a micro-grid. |

Lee et al. [45] | Time Series Prediction Using RBF Neural Networks with a Nonlinear Time-Varying Evolution PSO Algorithm | To integrate PSO algorithm in a neural network load forecasting model to find the optimal model parameters. | |

DE-NN | Amjady [46] | Short-Term Load Forecast of Microgrids by a New Bi-Level Prediction strategy | To develop a load prediction model by integrating neural network and evolutionary algorithm as feature selection technique and forecast engine. |

Ahmad et al. [47] | An Accurate and Fast Converging Short-Term Load Forecasting Model for Industrial Applications in a Smart Grid | To propose a combined DE and ANN algorithm to predict the industrial load in smart grid. | |

SA-NN | Khosravi et al. [48] | Construction of Optimal Prediction Intervals for Load Forecasting Problems | Investigated a model for prediction of load intervals instead of exact load values. |

K-shape clustering-NN | Hernandez et al. [49] | Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment | Presenting a solution for short-term load forecasting (STLF) in microgrids |

Quilumba [50] | Using Smart Meter Data to Improve the Accuracy of Intraday Load Forecasting Considering Customer Behavior Similarities | To apply the k-shape clustering method to identify the similar energy usage pattern among the users with smart meters before load prediction. | |

GSVM | Pai et al. [51] | Forecasting Regional Electricity Load Based on Recurrent Support Vector Machines with Genetic Algorithms | To investigate the feasibility of an ILF model tested on annual regional loads in Taiwan. |

Wu et al. [52] | A Novel Hybrid Genetic Algorithm for Kernel Function and Parameter Optimization in Support Vector Regression | Predicting electrical daily load using SVR with dynamic parameter optimization. | |

SA-SVM | Pai et al. [53] | Support Vector Machines with Simulated Annealing Algorithms in Electricity Load Forecasting | Elucidates the feasibility of using SVMs to forecast electricity load. |

PSO-SVM | Jiang et al. [54] | A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression with Hybrid Parameters Optimization | Presenting a hybrid PSO-SVM method for predicting load deviation in the distribution system. |

Ceperic et al. [55] | A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines | Improving an SVR based STLF model using PSO algorithm for hyper-parameter selection. | |

ABC-SVM | Hong [56] | Electric Load Forecasting by Seasonal Recurrent SVR (Support Vector Regression) with Chaotic Artificial Bee Colony Algorithm | Proposing an ILF model considering seasonal or climate changes and economic activities. |

Mat Daut et al. [57] | An Improved Building Load Forecasting Method Using a Combined Least Square Support Vector Machine and Modified Artificial Bee Colony | To improve the load forecasting performance by a new combined method. | |

HS-SVM | Ming Zeng et al. [58] | Short-Term Load Forecasting of Smart Grid Systems by Combination of General Regression Neural Network and Least Squares-Support Vector Machine Algorithm Optimized by Harmony Search Algorithm Method | Developing a heuristic hybrid algorithm used for LS-SVM for short-term load forecasting model. |

FOA-SVM | Li et al. [59] | Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm | To examine the feasibility of the LSSVM model to forecast annual electric loads. |

Cao et al. [60] | Support Vector Regression with Fruit Fly Optimization Algorithm for Seasonal Electricity Consumption Forecasting | Proposed a hybrid method combining SVM with FOA to forecast monthly consumption. | |

FA-SVM | Fard et al. [61] | A New Hybrid Modified Firefly Algorithm and Support Vector Regression Model for Accurate Short-Term Load Forecasting | To develop a SVR based model for STLF using FA algorithm to adjust the parameters. |

WT-SVM | Abdoos et al. [62] | Three Short-Term Load Forecasting Using a Hybrid Intelligent Method | Proposed a method for hourly heating load forecast integrating WT theory with ANN. |

Dataset Type | Description | Total Time Period | Recording Step |
---|---|---|---|

Commercial load of an office building in China [63] | outdoor temperature, humidity, and solar radiation were taken from climate database of a typical year in Guangzhou, China, while the hourly Cooling load consumption were simulated by software. | May, June, July, and August of a typical meteorology year. | 2 h |

Residential energy consumption dataset [64] | This dataset was collected from three different Campbell Creek homes. | the second week of September 2010 | 15 min |

Historical regional load data varies from 7 to 39 MW in a Microgrid [49] | Database contains raw data collected by several sensor networks (electric, weather, calendar, etc.) from the Spanish utility Iberdrola. | From 1 January 2008 to 31 December 2010 | Hourly |

Taiwan regional electricity load data [51] | The dataset includes regional electricity load data from 1981 to 2000 in Taiwan. | From 1981 to 2000 | Annual |

Public hourly electricity load and price time series datasets [65] | Australian energy market operator; 2012. Available at: http://www.nemmco.com.au New York independent system operator; 2012. Available at: http://www.nyiso.com | Daily load of the year 2005 | Hourly |

The electricity price and demand data [66] | New York independent system operator (NYISO) Electricity Market Data. Available: http://www.nyiso.com/ | From 1 January 2014 to 1 March 2014 | Hourly |

Heterogeneous data (history electricity load distributions, weather parameters, and season parameters) [67] | Load distributions per hour in four area: Los Angeles, California, New York City, Florida | 15 July 2015 to 10 September 2016. | Hourly |

Smart metering dataset [50] | Real-world smart meter data for residential customers of two different electric utility companies, from the United States and Ireland. | August 2009 to December 2010. | 30 min |

Almanac of Minutely Power dataset (AMPds) [25] | Minutely current readings of a Canadian household and its 19 appliances http://ampds.org/. | One year 2013 | Minutely |

Internet-based load dataset [19] | 1/Hourly load and temperature values, available at: ee.washington.edu/class/555/el-sharkawi/index files/Page3404.html 2/Daily peak load and temperature values available at: http://neuron.tuke.sk/competition 3/Half-hourly load, price, and temperature values available at: www.nemmco.com.au | 1/From 1 January 1985–31 March 1991 2/From 1 January 1997–31 January 1999 3/From 4 December 2001–31 December 2003 | 1/Hourly 2/Daily 3/Half-hourly |

Main Types | Method | Adv. | Dis. |
---|---|---|---|

Single method | FL | Decision making for uncertain information | Drawback of Cognitive uncertainties |

ANN | Unsupervised Learning | Overfitting | |

SVM | Structural Risk Minimization | Parameter uncertainty | |

GP | Optimal Search | Lack of Memory | |

PSO | Memory storage | local minimization drawback | |

Hybrid method | NF | Data characterization | Unknown optimal Number of clusters |

ANN-k-shape clustering | Feature extraction via unsupervised procedure | Unknown optimal Number of clusters | |

ANN-WT | Input selection | Only frequency resolution | |

SVM-FOA | Fast searching algorithm | complicated architecture | |

SVM-HS | Suitable for small sample and faster computational speed | complicated architecture |

Accuracy | Description |
---|---|

Mean Absolute percentage Error: $MAPE=\frac{1}{N}{\displaystyle \sum _{i=1}^{N}}\frac{\u2502{Y}_{i}-{\widehat{Y}}_{i}\u2502}{{Y}_{i}}\times 100$ | N: Number of samples ${Y}_{i}$: Actual data value $\widehat{{Y}_{i}}$: Predicted value |

Root Mean Square Error: $RMSE=\sqrt{\frac{1}{N}{\displaystyle \sum _{i=1}^{N}}{\left({Y}_{i}-{\widehat{Y}}_{i}\right)}^{2}}\times 100$ | N: Number of samples ${Y}_{i}$: Actual data value $\widehat{{Y}_{i}}$: Predicted value |

Coefficient of Determination: ${R}^{2}=1-\frac{{\sigma}^{2}\left(\widehat{Y}-{Y}_{i}\right)}{{\sigma}^{2}\left({Y}_{i}\right)}$ | ${\sigma}^{2}$: Variance of data ${Y}_{i}$: Actual data value $\widehat{{Y}_{i}}$: Predicted value |

${R}^{2}~1-\frac{RMS{E}^{2}}{Var\left({Y}_{i}\right)}$ | |

Mean Bias Error: $MBE=\frac{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$\left(N-1\right)$}\right.{\sum}_{i=1}^{N}\left({Y}_{i}-{\widehat{Y}}_{i}\right)}{{\widehat{Y}}_{i}}\times 100$ | N: Number of samples ${Y}_{i}$: Actual data value $\widehat{{Y}_{i}}$: Predicted value |

Method | Reference | MAPE |
---|---|---|

MLP | Khotanzad 2002 [91] | 2.87% |

MLP | Zhang Yun et al., 2008 [34] | 1.81% |

NF | Khotanzad 2002 [91] | 2.34% |

NF | Zhang Yun et al., 2008 [34] | 1.66% |

Method | Reference | MAPE |
---|---|---|

ANN | Hong-ze Li et al., 2013 [39] | 2.74% |

FOA-ANN | Hong-ze Li et al., 2013 [39] | 1.25% |

PSO-ANN | Hong-ze Li et al., 2013 [38] | 2.53% |

EA-ANN | Nima Amjady 2010 [46] | 2.95% |

WT-ANN | Nima Amjady 2010 [46] | 2.64% |

DE-ANN | Nima Amjady 2010 [46] | 2.4% |

**Table 7.**MAPE values of several hybrid and single algorithms developed for ILF by [58].

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**MDPI and ACS Style**

Fallah, S.N.; Deo, R.C.; Shojafar, M.; Conti, M.; Shamshirband, S.
Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. *Energies* **2018**, *11*, 596.
https://doi.org/10.3390/en11030596

**AMA Style**

Fallah SN, Deo RC, Shojafar M, Conti M, Shamshirband S.
Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions. *Energies*. 2018; 11(3):596.
https://doi.org/10.3390/en11030596

**Chicago/Turabian Style**

Fallah, Seyedeh Narjes, Ravinesh Chand Deo, Mohammad Shojafar, Mauro Conti, and Shahaboddin Shamshirband.
2018. "Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions" *Energies* 11, no. 3: 596.
https://doi.org/10.3390/en11030596