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Classiﬁcation and Regression Based Methods for

Short Term Load and Price Forecasting: A

Survey

Hira Gul1, Arooj Arif1, Sahiba Fareed1, Mubbashra Anwar1, Afrah Naeem1,

Nadeem Javaid1,*

Abstract Due to increase in electronic appliances, electricity is becoming basic

necessity of life. Consumption of electricity depends on various factors like tem-

perature, wind, humidity, weekend, working days and season. In electricity load

forecasting, many researchers perform data analysis on electricity data provided by

utilities to extract meaningful information. Smart Grid (SG) is power supply net-

work which allows consumers to monitor their energy usage. It integrates differ-

ent components of electricity like variety of operations, smart appliances, data col-

lected from smart meters and efﬁcient energy sources. To reduce the consumption

of electricity, accurate prediction is compulsory. A good forecasting model makes

an acceptable use of all characteristics of electric loads based data and also reduces

dimensionality of that data. Various machine learning techniques are proposed for

load and price forecasting in literature. In this research, we present a survey based on

different short term electricity forecasting techniques for price and load. We broadly

categorize different types of techniques into traditional machine learning and deep

learning techniques.

Key words: Load Forecasting, Support Vector Machine, Smart Grid, Price Fore-

casting, Deep Learning, Neural Network

1 Introduction

In this era of technology, energy consumption is expanding exponentially. Demand

of electricity is increasing day by day as number of electronic appliances are in-

Hira Gul

COMSATS University Islamabad, e-mail: hiragul001@gmail.com

Corresponding author

Nadeem Javaid, COMSATS University Islamabad e-mail: nadeemjavaidqau@gmail.com

1

2 Hira et al.

DATA PREPROCESSING

FEATURE SELECTION

FEATURE EXTRACTION

PREDICTION MODEL

05

04

03

02

06

HISTORICAL DATA

EXPERIMENT RESULTS

Fig. 1: General Forecasting Model

creasing. Consumption of electricity is divided into six areas: residential, industrial,

commercial, agricultural, transportation and other government. Residential area is

consuming 65% of total electricity [1]. Traditional grid is used as electromechani-

cal technology, which faces several difﬁculties like one-way communication, central

distribution, manual monitoring, few sensors and manual restoration. Smart Grid

(SG) is established to resolve the above-mentioned issues. It plays a leading role

in balancing the consumption, generation and transmission of electricity [2]. It is

power grid which monitors generation, transmission and consumption of electric-

ity [3]. It helps producers to produce required electricity according to consumers

need. So, forecasting of electricity is necessary for utilities to balance between de-

mand and supply of electric load [4].

Demand of electricity is dependent on numerous factors like humidity, wind,

temperature, season, weekend and weekdays. It also depends on number of house-

holders and their daily routine. Data analytics is procedure of assessing data by us-

ing statistical software to obtain useful information. In electricity load forecasting,

many researchers do data analysis on electricity data to extract meaningful infor-

mation. Many machine learning techniques are proposed for load forecasting. Large

amount of data is required for prediction. This data contains sensitive information

with thousands of entries. Accurate load forecasting reduces the electricity price as

it minimizes the consumption in peak hours [5].

Based on time interval, electricity load forecasting is split into three parts. First

is Short Term Load Forecasting (STLF), it forecasts the electricity from a time pe-

riod of 24 hours to one week. Second is Medium Term Load Forecasting (MTLF),

it forecasts load from a period of one week to one year. Third is Long Term Load

Forecasting (LTLF), it forecasts the load from a time period of one year to more than

two years [6]. Many load forecasting approaches developed in literature like Support

Classiﬁcation and regression based methods for STLPF 3

Vector Machine (SVM), linear regression and deep neural network. In deep learn-

ing, Convolutional Neural Network (CNN) is most commonly used method for load

forecasting. CNN consists of input layer, multiple hidden layers and output layer.

Hidden layer contains multiple layers like convolutional layers and pooling layers.

Any pooling layer can be used among max, average or sum pooling. [7]. Forecast-

ing models generally consist of six steps including historical data, preprocessing,

feature selection, feature extraction, prediction model and experimental results as

shown in ﬁgure 1.

2 Related Work

This section describes the related work of electricity forecasting for short term load

forecasting.

2.1 Problem Addressed

In this section, we describe the problem statement of related papers.

2.1.1 Machine Learning versus Deep Learning

Machine learning techniques are used for electric load forecasting. These techniques

are Random Forest (RF) and Gradient Boosting (GB). Although, these techniques

have some drawbacks. Computational complexity of RF is very high, as it takes

very long time to build decision tree. Error rates are very high in RF [2]. In [3], au-

thors discussed the problems of data redundancy in feature selection and extraction.

Existing models used only univariate data for price forecasting that is not sufﬁcient

for accurate forecasting. There is need of big data for electricity price forecasting

yet computational complexity of such data is very high. Over ﬁtting problem occurs

in decision tree which means that a model performs well on training data and its

performance degrades on testing data.

In existing studies, features directly fed into model without any preprocessing.

According to authors, parameter tunning is considered to be important for accu-

rate forecasting. So, there is need of feature selection and extraction technique for

load forecasting [4]. In [5], previous Recurrent Neural Network (RNN) models do

not use future hidden state vector and available past information. If a state vector

is generated incorrectly at any speciﬁc time then cannot be corrected at that spe-

ciﬁc time, as it is very important to enhanced forecasting at speciﬁc time. In [6],

many authors used optimization techniques still there is need to optimize the hybrid

models. Authors consider the problem of back propagation in hybrid approach com-

posed of DWT, EMD and RVFL. Back propagation is an algorithm which consists

4 Hira et al.

of two main problems: slow convergence and trapped in local minimum. In [8],

authors discussed drawbacks of ANN in DCANN. ANN has some disadvantages

like slow convergence, less generalizing ability, trapped in local minimum and poor

initialization of parameters.

2.1.2 Traditional Forecasting Methods

In [9], WD is only used for pre-processing and reconstruction results based on in-

dependent prediction. Such combination is not sufﬁcient for Support Vector Re-

gression (SVR). Moreover, the purpose of Multi-resolution Wavelet Decomposi-

tion (MWD) is need to be considered with SVR. In existing models, traditional ap-

proaches were used for load forecasting. These techniques are: time series and linear

regression. They mainly focus on aggregated electric load demand pattern. Now au-

thors are combining machine learning techniques with deep learning approaches.

So, there is need to consider the integrated deep learning techniques with combina-

tion of machine learning [10]. Short term day ahead load forecasting is very chal-

lenging job, because it depends on external environment factors like temperature,

wind and humidity. Existing day ahead load forecasting models improve the accu-

racy by compensating the computational cost. In short computational complexity of

hybrid model is a big challenge [11]. In [12], authors addressed the issue of data

redundancy among features in minimal Redundancy Maximal Relevance (mRMR).

They also discussed that inﬂuential factors like weather changes, day type and eco-

nomical condition affects the load forecasting. Authors ensemble different models to

address above mention problems. In [13], Linear regression and ARIMA gives best

results with linear problems while these models perform unsatisfactory with non-

linear time series data. Therefore, authors proposed a hybrid model which works

better for non-linear time series data. Time-Varying parameter Regression (TVR)

has a problem of price instability, ﬂuctuating input variables, managing availability

of data and complex data inputs [14]. In [15], authors discussed the limitations of

ANN like over-ﬁtting problem, trapped in local minimum and poor initialization of

parameters. To solve these issues, authors used combination of different models for

short term load forecasting. In [16], authors discussed that load forecasting is af-

fected by unstable factors like temperature, price, policy management and holidays

data. Due to ﬂuctuation in these factors, noisy data is generated which gives inaccu-

rate results. Authors used EMD to decompose original data into IMFs and forecast

the electric load. In [17], authors discuss the issue of cyber security in load forecast-

ing. Hackers injected false information in original dataset. Data integrity problems

are relatively new in this domain.

2.2 Solution Proposed

This section describes the proposed solution of related papers.

Classiﬁcation and regression based methods for STLPF 5

2.2.1 Machine Learning versus Deep Learning

In [2], authors proposed Grey Correlation Analysis (GCA) for feature selection

to remove the repetition of features. They combined Kernel Function with Prin-

ciple Component Analysis (KPCA). They used Differential Evolution (DE) which

is based on Support Vector Machine (SVM) classiﬁer for the classiﬁcation of price

forecasting. In [3], authors proposed probability density forecasting model for short

term power load forecasting. Authors proposed Multi-Layer Perceptron (MLP)

function with deep neural network. Kernel density estimation method consist of

three sub modules deep learning, loss function and quantile regression.

In [4], authors proposed a deep learning model called Stack Denoising Auto en-

coders (SDAs) for feature extraction. This model train SVR to forecast the electric

load of day ahead. By using heterogeneous deep model, accuracy of forecast is

better with less errors. In [5], authors proposed RICNN which is combination of

RNN and 1- Dimensional Convolutional Neural Network (1-D CNN). 1-D CNN

inception module is used to adjust the prediction time and hidden state vector val-

ues. They calculate the hidden state vector from adjacent time steps. As a conse-

quence, RNN generates optimized and robust network through prediction time and

hidden state vector. In [6], authors proposed hybrid incremental learning method.

In this method three techniques are combined: Discrete Wavelet Transform (DWT),

Empirical Mode Decomposition (EMD) and Random Vector Functional Link net-

work (RVFL). RVFL is very useful as it generates weights randomly among input

layer and hidden layer. It also provides nearest possible solution for parameters cal-

culation. In incremental learning, when they add DWT and EMD it impressively

increases accuracy and effectiveness of short term load forecasting. In [8], authors

proposed a model named Dynamic choice artiﬁcial neural network (DCANN) which

selects input dynamically for ANN. DCANN is hybrid model which consists of su-

pervised and unsupervised learning problems. This approach selects unsupervised

learning technique to choose input variable for individual output. Authors train cor-

respondence inputs and outputs through supervised learning.

2.2.2 Traditional Forecasting Methods

In [9], authors proposed hybrid incremental learning method. In this method, three

techniques are combined: DWT, EMD and Random Vector Functional Link net-

work (RVFL). RVFL is very useful as it generate weights randomly among input

layer and hidden layer. It also provide nearest possible solution for parameters cal-

culation. In incremental learning, when authors add DWT and EMD it impressively

increases accuracy and effectiveness of short term load forecasting. In [10], uthors

proposed an algorithm which is based on deep neural network for STLF. In this

algorithm there are input layer and output layer. Input layers denote the past data

while output layer denote the future energy load. There is a deep energy which has

two main processes: feature extraction and forecasting. In feature extraction, there

are three more layers of convolutional layers and three pooling layers. In [11], au-

6 Hira et al.

thors proposed a hybrid ANN day ahead based load forecasting model for smart

grid. Proposed model consists of three components: pre-processing module, fore-

cast module and optimization module. In preprocessing module, irrelevant variable

and data redundancy are removed from input sample. In forecast module, ANN is

used with sigmoid function and multivariate auto regression algorithm.

In optimization module, heuristic problem solving approach is used to reduce

error. For this purpose they used enhanced differential evolution algorithm. In [12],

authors proposed hybrid model EMD-mRMR-FOA-GRNN which is combination of

Empirical Mode Decomposition (EMD), minimal Redundancy Maximal Relevance

(mRMR), fruit Fly Optimization Algorithm (FOA) and General Regression Neural

Network (GRNN). Firstly, they divide load series data into Intrinsic Mode Func-

tions (IMFs), secondly, mRMR is applied to select the best features. Finally, FOA

is used to enhance the factors in GRNN. In [13], authors proposed AS-GCLSSVM

hybrid model for short term load forecasting. AS-GCLSSVM stands for Autocorre-

lation feature selection and Least Squares Support Vector Machine wolf algorithm

and cross validation. In [14], authors proposed Hybrid Iterative Reactive Adaptive

(HIRA) model. This model consists of two steps. In ﬁrst step, they identiﬁed only

those parameters which affect the electricity prices. In second step, selected vari-

ables are used in price forecasting by applying hybrid approach. This HIRA model

is combination of statistical models and neural network tools. In [15], authors pro-

posed Wavenet ensemble model for STLF. In this model, authors combined different

components like input parameters, mean, median , mode, cross-validation, selection

and algorithms. Authors predicted hourly load forecasting through one step ahead

strategy. In [16], authors proposed a short term forecasting model named as EMD

Mixed ELM. In this model, authors combined EMD and Extreme Learning Machine

(ELM). They used EMD for denoising and normalization of complicated features

from a large dataset. The performance of ELM depends upon type of kernel they

used. Authors used mixed kernel for ELM. Mixed kernel is combination of UKF

kernel and RBF kernel. In [17], authors proposed framework for load forecasting

named as systematic data integrity simulation.

2.3 Future Challenges/ Open Research Issues

This section discusses the limitation and future challenges of literature.

2.3.1 Machine Learning versus Deep Learning

In [2], authors used RF for decision tree. Computational complexity of RF is very

high, as it takes very long time to build decision tree. DE is used for tunning pa-

rameters, however it is prone to slow convergence in local optima. In future, authors

will apply real time requirements in the proposed model. In [3], authors used RNN

model which do not use future hidden state vector and available past information. If

Classiﬁcation and regression based methods for STLPF 7

a state vector is generated incorrectly at any speciﬁc time then cannot be corrected,

as it is very important for enhanced forecasting in prediction time. In [4], authors

did not specify the condition on which basis they extract the features. They did not

consider many important parameters like temperature, wind and humidity. In [5],

authors used EMD and it has mode mixing problem. ELM is feedforward neural

network which is most widely used, although drawback of ELM is it does not up-

date weights and parameters . In future, authors will combine proposed model with

ANN and SVR and applied on real time applications to evaluate the performance [6].

In [8], main limitation of the paper is computational power which is very high. The

classiﬁer consumes more resources than existing models. Authors did not validate

that generated inputs are corrected or not.

2.3.2 Traditional Forecasting Methods

In [9], there is need to tune the parameters for better results. Also, different selec-

tion and extraction techniques can be used for different buildings in order to get

more better results. For seasonal data prediction, there is need to provide more sam-

ple data that is required to increase the consistency of training data. In future, this

model can be applied on different regions to attain accurate electricity forecasting of

that regions. In [10], authors concluded that due to complex neuron structure in neu-

ral network the computational power is very high as compared to existing models.

Three layers of pool makes the model more complex. Over ﬁtting problem arises

which affects the training data. In [11],an enhanced signal processing technique for

features selection and extraction and some optimization technique needs to be con-

sidered for scheduling based application. In [12], prediction performance reduced

due to poor generalization capability of GRNN. Computational complexity of pro-

posed model is also high. In [13], authors consider AutoCorrelation Function (ACF)

relationship between two parameter. However, additional external parameters like

temperature, weather, holidays and festival related parameters need to used. In [14],

computational time is high of proposed model. It is because four different models

are combined together. This model performs well on big data but worst on small

datasets. In [15], authors compared ensemble learning algorithms with base learner

classes like deep neural network or ELM. However, they did not validate the af-

fect of feature selection method. ELM as being a feedforward neural network have

prominent role in SG operations besides this, It needs to be intensiﬁed [16].

8 Hira et al.

Table 1: Related work.

Problem Identiﬁed Proposed Solu-

tion

Results Limitations

In [2], computational

complexity of RF is very

high, as it takes very

long time to build deci-

sion tree

GCA for feature

selection is proposed

to remove the rep-

etition of features

and they combined

KPCA.

Proposed model gives

98% accuracy and shows

more robustness as com-

pared to NB and DT

DE is used for

tunning parameters,

however, it shows

convergence at local

optima

In [3], over ﬁtting prob-

lem occurs in decision

tree, which means deci-

sion tree performs good

in training but not in pre-

diction

Probability density

forecasting model is

proposed

The error rate of proposed model is less

than RF and GB

RNN models do

not used future

hidden state vector

and available past

information.

In [4], features directly

move into model with-

out any preprocessing,

for accurate results tun-

ning parameter is neces-

sary

SDAs model is pro-

posed by authors

Error rate of proposed

model is less than plain

SVR and ANN

Authors did not ex-

plain the rules on

which basis they per-

form features selec-

tion and extraction

In [5], previous Re-

current Neural Network

(RNN) models do not

used future hidden state

vector and available past

information

Authors proposed

RICNN

Value of MAPE is 4.779

while the values of

MAPE in benchmark

techniques are 8.084,

7.371 and 5.634. These

values show that pro-

posed model is more

accurate than existing

models

RNN consumes

more time for

training the dataset

and deduction of the

results as compared

to MLP and CNN

In [6], the problem

of back propagation in

hybrid approach com-

posed of DWT, EMD and

RVFL

Authors proposed

hybrid incremental

learning method.

In this method

three techniques are

combined: DWT,

EMD and RVFL

Value of RMSE is

218.329 while the values

of MAPE in benchmark

techniques are 355.503

for GLMLF, 307.892

for SHLFN, 278.511

for RF and 244.820

for EMD-RVFL. These

values show that pro-

posed model is more

accurate and efﬁcient

than existing models

This model will de-

composed with vari-

ous other model like

deep learning, sup-

port vector regres-

sion and kernel ridge

regression

In [8], drawbacks of

ANN in DCANN. ANN

has some disadvantages

like slow convergence,

Less generalizing perfor-

mance, trapped at local

minimum and poor ini-

tialization of parameters

DCANN is proposed

which selects input

dynamically for

ANN

Results of proposed

model with dynamic

selection are 10.71%

and 8.39%. These values

show that proposed

model is more accurate

and than existing models

Main limitation of

the paper is compu-

tational power which

is so high

Classiﬁcation and regression based methods for STLPF 9

In [9], WD only used

for pre-processing and

reconstruction results

based on independent

prediction. Such combi-

nation are not sufﬁcient

for SVR

Authors proposed

hybrid incremental

learning method.

In this method,

three techniques are

combined: DWT,

EMD and RVFL

Authors compared pure

SVR and hybrid SVR

and experiment shows

that addition of MWD in-

creases the accuracy of

forecasting

For seasonal data

prediction, there

should be more

sample data required

to increase the con-

sistency of training

data.

In [10], need to con-

sider the integrated deep

learning techniques with

combination of machine

learning

A algorithm which is

based on deep eural

network is proposed

for STLF

The results of previous

models are 9% and

11% while, Results of

proposed model are

9.77% for MAPE and

11.66% for RMSE. Au-

thors claimed that they

achieved high accuracy

Due to too much

neural network

the computational

power is very high as

compared to existing

models

In [11], existing day

ahead load forecasting

model improves the ac-

curacy by compensating

the computational cost.

Computational complex-

ity of hybrid model is a

big problem

Authors proposed

a hybrid ANN day

ahead based load

forecasting model

for smart grid

Value of MAPE is

1.23 while values of

existing models are

3.18 and 2.31

FS technique is not

performs satisfac-

tory, it needs more

reﬁnement

In [12], authors ad-

dressed the issue of data

redundancy among fea-

tures in mRMR

Authors proposed

hybrid model EMD-

mRMRFOA-GRNN

Performance of EMD-

mRMR-FOA-GRNN

model is better than

existing models

Prediction perfor-

mance reduced due

to poor generaliza-

tion capability of

GRNN

In [13], Linear Regres-

sion and ARIMA gives

best results with lin-

ear problems while these

models perform unsatis-

factory with non-linear

time series data

Authors proposed

AS-GCLSSVM

hybrid model

Values of MAPE, MAE

and B2are 0.5596,

32.2088 and 0.9952.

Based on results authors

concluded that proposed

model is better than

existing model

Computational com-

plexity of proposed

model is high be-

cause of GWO algo-

rithms

In [14], over-ﬁtting

problem, trapped in

local minimum and

poor initialization of

parameters

Wavenet ensemble

model for STLF

Performance of WNN

ensemble model is better

than existing models

Authors will com-

pare ensemble

learning algorithms

with base learner

class like deep

neural network or

ELM

In [15], the limitation

of ANN like overﬁtting

problem, trapped in local

minimum and poor ini-

tialization of parameters

Proposed Wavenet

ensemble model for

STLF

Performance of WNN

ensemble model is better

than existing models

Authors will com-

pare ensemble

learning algorithms

with base learner

class like deep

neural network or

ELM

10 Hira et al.

In [16], EMD is used

to denoise original sig-

nals. However, it has

mode mixing problem

and ELM cannot update

the weights and biases

Authors combined

EMD and ELM

Values of MAE, RMSE,

MAPE and TIC are

7.3550, 9.5823, 08093

and 0.0052 perspec-

tively, Performance of

EMD-mRMR-FOA-

GRNN model is better

than existing models

Prediction perfor-

mance reduced due

to poor generaliza-

tion capability of

GRNN

In [17], Linear Regres-

sion and ARIMA gives

best results with lin-

ear problems while these

models perform unsatis-

factory with non-linear

time series data

Authors proposed

AS-GCLSSVM

hybrid model for

short term load

forecasting

Values of MAPE, MAE

and B2are 0.5596,

32.2088 and 0.9952,

Based on results authors

concluded that proposed

model is better than

existing model

Computational com-

plexity of proposed

model is high be-

cause of GWO algo-

rithms

3 Conclusion

Demand of electricity is increasing exponentially as number of electronic appli-

ances are increasing day by day. Different forecasting models were proposed in last

decade. In this paper, we conduct a survey based on load and price forecasting. We

discussed traditional machine learning approaches and deep learning approaches.

In this research, we compare the performance of different load and price models

to observe the best results. The research has been moving towards new and more

efﬁcient techniques and replacing old approaches. There is a clear move towards

hybrid techniques techniques. Hybrid techniques are efﬁcient, better computational

complexity and more ﬂexible. This research brings open challenges for future.

Classiﬁcation and regression based methods for STLPF 11

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