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Electricity Load and Price Forecasting using

Machine Learning Algorithms in Smart Grid: A

Survey

Arooj Arif, Nadeem Javaid, Mubbashra Anwar, Afrah Naeem, Hira Gul, and

Sahiba Fareed

Abstract Conventional grid moves towards Smart Grid (SG). In conventional grids,

electricity is wasted in generation-transmissions-distribution, and communication

is in one direction only. SG is introduced to solve prior issues. In SG, there are

no restrictions, and communication is bi-directional. Electricity forecasting plays a

signiﬁcant role in SG to enhance operational cost and efﬁcient management. Load

and price forecasting gives future trends. In literature many data-driven methods

have been discussed for price and load forecasting. The objective of this paper is

to focus on literature related to price and load forecasting in last four years. The

author classiﬁes each paper in terms of its problems and solutions. Additionally, the

comparison of each proposed technique regarding performance are presented in this

paper. Lastly, papers limitations and future challenges are discussed.

1 Introduction

Smart Grid (SG) is a setup that allows the devices to communicate between supplier

and consumer. SG aims to calculate the best generation- transmission-distribution

patterns, save energy and reduce cost [1]. Electricity forecasting plays a signiﬁcant

role in SG [2].

The most common task in SG is to forecast electricity. Accurate forecasting helps

in rationally scheduling the electric generator, which is beneﬁcial for reducing pro-

duction cost and saving electric power [3].

Due to deregulated electricity market, the dynamics of electricity trade is com-

pletely changing. Electricity encompasses a set of characteristics that are unusual

to alternative markets such as load are closely related to environmental factor like

weather conditions, unexpected price peaks. Electricity has become a central re-

Arooj Arif, Arooj Arif, Nadeem Javaid (Corresponding Author), Mubbashra Anwar, Afrah Naeem,

Hira Gul, Sahiba Fareed

COMSATS University Islamabad, email: nadeemjavaidqau@gmail.com

1

2 Arooj et al.

search area in the energy sector due to its distinctive behaviour. Electricity forecast-

ing is one of the most signiﬁcant challenges faced by different market participants

and electricity market is build to make grid stable. As a result of uncertain predic-

tion, the stability of grid compromised and increases the risk of blackout [4]. As

Figure 1, shows different steps of forecasting.

Machine Learning (ML) techniques are well-known techniques in electricity

forecasting. Different machine learning algorithms proposed for price and load fore-

casting.

ML seems like a tool to deal with a signiﬁcant quantity of data generated in an

electric grid. Machine Learning Algorithms (MLAs) gives a systematic way to an-

alyze the data and make appropriate decisions to run the grid. MLA functionalities

include price and load forecasting, fault detection, power generation, future opti-

mum schedule during data breach [5].

MLAs focus on to build programs that learn from experiences. The purpose of

MLA is to provide automatic information learning from raw data that are used in

the decision-making process to implement advanced predictive models [6].

In addition, ML is divided into four categories: Supervised - (SL) algorithm,

Unsupervised-Learning (UL) algorithm, Semi-Supervised- Learning (SSL) algo-

rithm, and Reinforcement Learning (RL) algorithm. SL algorithms train on labeled

data; however, all data is unlabeled in UL. In SSL algorithms, some data is labelled,

and signiﬁcant data is unlabeled, while RL algorithms learn through delayed feed-

back by interacting with environment [6].

SL classiﬁed into two categories: classiﬁcation and regression. In classiﬁcation,

output variables are often called labels such as ”drama” or ”movie,” etc. Contrarily,

output variables of regression are real values or ﬂoating-point values. These are

often quantities such as amount and size. In simple words, classiﬁcation predicting

a discrete class and regression predicting a continuous quantity [6].

Clustering, association and anomaly detection are USL techniques. In clustering,

Deep Learning (DL) model identify similarity and group them in the same feature.

In association, USL model identiﬁes the critical feature that is correlated with one

another. Anomaly detection identiﬁes false data by noticing abnormal patterns [6].

SSL applies both on labelled and unlabeled data. Active learning is a type of SSL

in which model is trained with a limited number of labels [6].

RL is an iterative process to predict the best next step to perform task. It is suit-

able for games such a ”packman” or ”tic-tac-toe”, playing a game against an oppo-

nent team or train a computer to drive a car [6].

DL is a subset of MLA. It is a well-known subset of ML due to its unique ability.

In MLA, designers manually adjust, if MLA gives unprecise prediction; however,

DL algorithm automatically concludes if forecast is precise or not [6]. There are

many deep learning models reviewed in literature.

The remainder of the paper is structured as follows: Section 2, related to problems

of each paper is addressed. In Section 3, the papers contribution are summarized. In

Section 4, different performance metrics are mentioned that are used for evaluation.

Limitations in the literature and future challenges are discussed in Section 5.

Electricity Load and Price Forecasting using Machine Learning Algorithms . 3

Fig. 1: Steps of Forecasting

Deﬁne the Problem

First you need to do is, ﬁnd the

problem.What is your exact

problem,that you need to solve.At

the end of this stage, you have all of

the information and context you

need to solve this. problem.

Data Mining

Once you deﬁne the problem, then you need

data to turn the problem around with a solution.

You ﬁnd ways to get that data,

DataClean & Explore

Oftentimes, data has not been

well-maintained. You will see errors

that will corrupt your analysis. When

you normalize your data then you can

start playing with it.

Feature Engineering

Select meaningful feature from raw data.

Feature Selection and Feature Extraction is

done in this phase text here

Predictive Modeling &

Data Visualization

Train machine learning

algorithms.,evaluate the

performance and use them for

predictions.See the results using

plots

2 Problems Addressed

2.1 Load Forecasting

Electricity forecasting plays a vital role in SG. Different forecasting models exist

to forecast electricity load [2]. Very-Short-Term- Load-Forecasting (VSTLF) pre-

dicts the load from minutes to hours. Short-Term-Load-Forecasting (STLF) forecast

the load from hour to atleast one week ahead [7, 8]. Long-Term-Load-Forecasting

(LTLF) estimates year ahead load [7]. Accurate forecasting is very challenging task,

if load is non stationary [8]. In [7], the issue addressed in Korean university build-

ings to forecast the electric load accurately. In Korea, educational buildings have

the highest electric load. It shows diverse energy-consuming patterns depends on the

semester and their diverse holidays. Moreover, load patterns also depend on various

features such as events, weather, and academic schedule. In [3], the problem has

declaimed that some machine learning algorithms have been carried out for load

forecasting algorithms. However, features are directly used to train a model without

preprocessing separately.Day-ahead load forecasting is a common task in electri-

cal grids. Accurate and proper forecasting model is necessary to save energy and

reducing cost. In [9], authors highlight that accurate load demand forecasting is a

challenging task due to some exterior factors which cause data to be unpredictable.

Artiﬁcial Neural Network (ANN) has been successfully used for classiﬁcation and

4 Arooj et al.

regression problems, however, it is often trap in local minima. In [10], authors ad-

dress that each model has its own behaviour, so we can not get the accurate results

on the basis of individual models.

An electricity forecasting method for region and city is a severe problem. As

the development of region and city relies on the betterment of electricity market.

So accurate electric load forecasting model is needed. In [11], authors highlight

that, it is very challenging to deal with nonlinear patterns in load forecasting. Al-

though in Singlehidden-Layer Feedforward-Neural network (SLFN), weights are

traditionally tuned by Back Propagation(BP). Although it is time-consuming and

their gradient boosting is also often trapped in local minima. In [12], authors ad-

dressed that traditional short term load forecasting models are widely used, there

are still some outstanding problems; e.g. it is difﬁcult to simulate the relations be-

tween variables affecting load through mathematical model, forecasting results re-

quire improvements, forecasting results are not reﬂected in real-time situations. In

[13], authors adressed that electricity load forecasting is very challenging as many

factors affects such as weather conditions and researchers proposed many models

to deal with this problem. In general models are divided into TS (Time Series) mod-

els and AI (Artiﬁcial Intelligence) models. Each model has its own pros and cons

and individually these models does not give accurate results. Authors addressed that

many STLF models have been developed for electricity load forecasting as per [14].

They classiﬁed STLF models into two sections: traditional models and AI models.

Traditional models are theoratically based on linear deﬁnition while AI models have

nonlinear computing capability. However, there is need to combine these models to

achieve higher and satisﬁed accuracy. In [15], authors highlight that SVR is very

popular in providing satisfactory results; however, SVR suffering from local minima,

when evolutionary algorithms decide its parameters that is the biggest drawback of

SVR. Moreover, robustness does not recieve satisﬁed level.

2.2 Price Forecasting

In the electric power industry, accurate price forecasting is a valuable tool for mar-

ket participants. Electricity price has many complex features(non-stationary, high-

volatility, price spikes, non-linearity) that makes forecasting very difﬁcult. High-

quality price forecasting plays an essential role in transmission expansion and de-

cisions of power investment [16]. Authors in [17], discussed that the selection of

optimized model is not only challenging to improve the accuracy of price forecast, it

is also concerned problem of electricity market participants due to heavily depend

on electricity. A lot of hybrid models have been developed through different op-

timization techniques; however, researchers aim to determine appropriate features

for accurate electricity forecasting. In [18], authors addressed that accurate price

forecasting is very challenging due to its complex features and price spikes. Many

models for price forecasting exist in literature, and authors highlight two main is-

sues. The ﬁrst problem is that EMD combine with hybrid model gives the end effect

Electricity Load and Price Forecasting using Machine Learning Algorithms . 5

Table 1: Related Work of Short Term Load Forecasting.

Validation Comparison Techniques

MAPE [3] SVR, ANN

Time Series Cross Validation,

MAPE, MAE, RMSE [7]

TBATS, DT, Multiple Regression,

Gradient Boosting Machine, SVR, ANN

RMSE, MAPE [9] SVR, ANN, DBN, RF, EDBN, EMD-SVR,

EMDSLFN, EMD-ANN, EMD-RF

AE, MAE, RMSE, MAPE [10] MFES, ESPLSSVM, Combined Method

based on ANFIS and NN

MAPE [11] ANN, RF, mRVFL, EMD-ANN, EMD-RF,

EMDRVFL

RMSE, NMSE, MAE, MAPE [12] ARMA, BPNN, KNN

RMSE, MPE, MAE, MAPE [13] ENN, ELM, SVN

RMSE, MAE, MAPE [14] SVR, SVR-PSO, SVR-GA

RMSE, MAE, MAPE ,AIC, BIC [15] AFCM, SVR, SVR-PSO, SVR-GA

which affects the accuracy of EMD. Second, hybrid model can not give accurate

and effective results when combining with linear and non-linear model. In [19], au-

thors highlight that, as the integration of renewable energy increases, volatility in

price also increases and market participants behaviour become more unpredictable.

The electrical grid becomes unstable due to improper balance of generation and

consumption. While there are many price forecasting models; however, the area of

deep learning is still unexplored, and extensive benchmarks are also missing for

predicting electricity prices. Traditionally fundamental models forecast electricity

price by describing demand and supply as accurate as possible as per [20]. Highly

inconsistent electricity generation from Renewable Energy Resources (RES) is a

challenging factor. Fundamental time series are based on assumptions, e.g. auto-

correlation and volatility clustering; however, they ignore the relation of price to

fundamentals. In [21], authors addressed that existing price forecasting methods are

unable to handle a tremendous amount of price data. Feature selection did not help

to remove redundancy.

Furthermore, integrated infrastructure is also lacked for synchronize the methods

of electricity prices. The stability of electricity is discussed in [22], where authors

highlight that electricity price is highly unstable in electricity market due to various

visible, and invisible factors and it is further increased by the deployment of SG. So,

accurate short term forecasting is necessary as it is beneﬁcial both for consumer and

seller. In [23, 24, 25, 26, 27, 28, 29], authors highlight the issues in price and load

forecasting.

6 Arooj et al.

Table 2: Related Work of Short Term Price Forecasting.

Validation Comparison Techniques

MAPE, MAE [17] BPANN, LSSVM

MAPE, RMSE,Theil U statistic1 [18],

Theil U statistic2 WARMA-BP, WARMA-LSSVM,W-ARMA-KELM

sMAPE [19] DNN,GRU,LSTM, Moving Average Models, Machine Learning vs

Statistical Methods, Hybrid Models,TBATS

MAD, RMSE [20] ARIMA, RMA, GARCH (AR-GARCH)

Different values of threshold [21] HFS+DE-SVM, KPCA+DE-SVM, DE-SVM, DT, RF

MAPE[22]

Mixed Model, ARIMA with 2 Variables,Neural Networks,

Weighted Nearest Neighbour, Wavelet-ARIMA with 4 Variables,

Fuzzy Neural Network, Adaptive Wavelet Neural Network

3 Contributions

3.1 Dataset

In [7], KEPCO and KMA dataset is used to evaluate the proposed model. USA

dataset is applied to test the model in paper [3]. In paper [9, 10, 11, 13, 14, 15],

authors used AEMO dataset to evaluate the forecasting model. NEM dataset is used

in paper [12]. In [17], authors put in AEM and PJM dataset for the evaluation of

proposed model. Spanish Market and Australian Market dataset are taken in paper

[18] for evaluation. In paper [19, 20], EPEX dataset is used. ISO-NE dataset is used

in paper [21]. Authors put in Iberian dataset in paper [22]. NYISO dataset is applied

for performance evaluation in [13, 14, 15]. As Figure 2 graphically represent the

datasets.

3.1.1 Critical Analysis

NYISO dataset and AEMO dataset are extensivley used to test the forecasting mod-

els in Section3.1.

3.2 Load Forecasting

In [7], authors come up with short term load forecasting model for the university

campus. They developed a two-stage predictive model based on Simple Moving

Average (SMA) and Random Forecast (RF). They collected two clusters of data.

Electricity Load and Price Forecasting using Machine Learning Algorithms . 7

Fig. 2: Distribution based on Dataset

short term load forecasting

Dataset

Dataset

Dataset

Your text here

Electricity Price and Load

Forecasting

Dataset + Classiﬁer

NYISO

EPEX

ISO-NE

PJM

AEMO

Dataset

Dataset

short term load forecasting short term price forecasting short term price forecasting

short term load forecasting

Hybrid Model (IEMD, ARIMA,

WNN) ,(ARIMA + (RF, SVM,

LOWESS, GLM)),PSRBSK,

EMD-PSO-GA-SVR

Hybrid GRU-DNN, Hybrid

LSTM-DNN,CNN, DNN as an

extension to traditional MLP,ANN

DE-SVM DCANN, updated DCANN

EMD based DBF,

EMD-Mixed-ELM, Hybrid

Approach based on DWT, EMD

and RVFL

The ﬁrst cluster consists of thirty-two buildings with academic purpose and the sec-

ond cluster consists of sixteen dormitory buildings. Another data also considered

relevant to academic schedule, weather, and calendar. In [3], authors discussed a

framework consists of two modules: feature extraction module and load forecasting

module. Feature extraction module has three subnetworks: the ﬁrst subnetwork re-

ceives previous day’s electric load, the second subnetwork considers the latest three

days’hourly electric load, and the last subnetwork is designed for weather param-

eters. The extraction of features by using Stack Denoising Autoencoders (SDAs),

were concatenated with a season parameter and then train Support Vector Regres-

sion (SVR) model for electricity forecasting. In [9], authors presented a proce-

dure in which TS is decomposed into several Intrinsic-Mode-Functions (IMFs) and

one residue by Empirical-Mode-Decomposition (EMD) method. For each IMF and

residue, build a training matrix for one Deep Belief Network (DBN). Train DBN

to obtain results for each extracted IMF and residue. The predicted results summed

to formulate ﬁnal prediction. In [10], authors proposed an STLF model based on

EMD-Mixed-ELM. EMD is used to decompose the load series and to capture the

complicated features and denoise the data, then apply denoise data to mix kernel-

based ELM. Radial-Basis-Function (RBF) and Unscented Kalman Filter (UKF) ker-

nel are used in mixed kernel method. In [11], authors adopted an ensemble approach,

DWT-EMD based incremental Randomized Version Functional Link (RVFL) net-

work. In [12], authors used a novel STLF model based on the Weighted- K-Nearest-

Neighbor (W-K-NN) algorithm to achieve accuracy. The authors proposed a hybrid

8 Arooj et al.

model in paper [13]. The main focus of this paper to propose a robust model that

can be used for different types of hours, days, and different types of markets that

will attract many market participants. In paper [14] authors proposed STLF model

by hybridizing Phase-Space-Reconstruction (PSR) and Bi-Square-Kernel (BSK) re-

gression model. A hybrid EMD-PSO-GA-SVR forecasting model is proposed in

paper [15]. The objective of this model to overcome the drawback of SVR model

that gives poor results on large dataset.

3.3 Price Forecasting

Authors in [17], proposed DCANN and its updated version. It consists of super-

vised and unsupervised components. The supervised component aims to absorb

knowledge from previous data using ANN and OA, while an unsupervised com-

ponent can delete bad samples by building an IBSM. In [18], authors proposed

improved EMD to reduce loss of information and extract better characteristics

of electricity price. Furthermore, an integrated model combines with Exponential

Generalized Autoregressive Conditional Heteroscedasticity (EGARCH), Adaptive

Network-based Fuzzy Inference System (ANFIS), Auto-Regressive Moving Av-

erage with exogenous variables (ARMAX), and Improved Empirical Mode De-

composition (IEMD) is proposed for day-ahead price forecasting. In [19], authors

described a deep learning modeling framework consists of four models: a hybrid

GRU-DNN, hybrid LSTM-DNN, CNN, DNN as an extension to traditional Multi-

Layer Perceptron (MLP). In [20], authors proposed ANN model to forecast elec-

tricity prices. In [21], authors proposed an integrated framework for electricity price

forecasting. The framework consists of Grey Correlation Analysis (GCA) based hy-

brid Feature Selector (HFS), combining RF and ReliefF. KPCA is used for feature

extraction. Differential evolution based SVM also designed to tune parameters of

SVM. In [22], authors proposed ARIMA combine with different machine learning

techniques.

3.4 Feature Selection/Extraction

In [3], authors proposed SDAs for feature extraction module. SDAs consists of au-

toencoders in each layer. The output of each layer is the input of next layer. In

[9, 10, 15], EMD is implemented to extract features. EMD breaking down signal

into IMFs without leaving time domain. EMD is useful for analysing the signal that

is non-stationary. In [11], feature extraction done by DWT-EMD. EMD has mode

mixing problem. DWT decomposes TS signals into frequency components. DWT

solves frequency mixing problem of EMD. In [12], authors calculate Euclidean Dis-

tance for feature selection. In [17], authors select best inputs using Index of Bad-

Sample-Matrix (IBSM). IBSM is used to delete bad training samples. In [18], IEMD

Electricity Load and Price Forecasting using Machine Learning Algorithms . 9

Table 3: Feature Selection and Extraction Techniques.

Dataset FeatureExtraction and Selection

USA [3] SDAs

AEMO [9] EMD

AEMO [10] EMD

AEMO [11] DWT-EMD

NEM [12] Euclidean Distance

NYISO, AEMO [13] IEMD, PC

NYISO, AEMO [14] PSR

NYISO, AEMO [15] EMD

AEM, PJM [17] IBSM

Spanish Market,

Australian Market

[18]

IEMD

EPEX [20] KNN

ISO-NE [21] KPCA, Relief-F, RF

is used to extract linear and non-linear components accurately. In [20], KNN is used

as a ﬁlter method, it is not only considered the relationship between input and output

variables. Furthermore, it can identify non-linear relationships in dataset. In [21],

KPCA is implemented for feature extraction to minimize the redundancy among

features. For feature selection, authors proposed GCA based hybrid feature selec-

tion (Relief-F and RF). It is fusion of RF and Relief-F. In [13] IEMD is implemented

to extract features and Pearson’s correlation coefﬁcient (PC) is used to select fea-

tures. Fruit-Fly-Optimization-Algorithm (FOA) is used to optimize the parameters

of Wavelet-Neural- Network (WNN). In [14], PSR algorithm is used to extract the

valuable features to improve forecasting results.

3.4.1 Critical Analysis

EMD is widely used for feature extraction in Section 3.4.

4 Validation

4.1 Load Forecasting

In [7], authors applied Time Series Cross-Validation to evaluate the performance of

the proposed model. Several performance metrics such as Mean Absolute Percent-

age Error (MAPE), Root Mean Square Error (RMSE), and Mean Absolute Error

(MAE) are also used to predict the accuracy of forecasting model. To evaluate model

10 Arooj et al.

in [3], authors used MAPE as a performance indicator. In paper [9], the accuracy of

the model examined via RMSE and MAPE. In [10], four metrics such as Mean

Error (AE), MAE, MAPE, and RMSE are presented. MAPE is applied to evaluate

the performance of proposed model in [11]. Accuracy metrics such as RMSE, Nor-

malized Mean Square Error (NMSE), MAE, and MAPE are used for evaluation in

[12]. In [13], MAPE, RMSE, MAE, and MPE are used for model evaluation. The

results in paper [14], forecasting the errors in terms of MAPE, RMSE and MAE.

In [15], RMSE, MAE, MAPE, AIC, and BI evaluation metrics are used to measure

forecasting errors.

4.2 Price Forecasting

In [17], MAPE and MAE are used to evaluate the performance of model. In [18],

the four evaluation metrics are used to evaluate the performance, which includes

MAPE, RMSE, Theil U statistic1, and Theil U statistic2. In [19], symmetric Mean

Absolute Percentage Error is used to evaluate the performance of forecasters. In

[20], two metrics are used to evaluate the performance, that is MAD and RMSE. In

[21], different values of the threshold are used to control feature selection. In [22],

validation is done through MAPE.

4.2.1 Critical Analysis

MAPE, RMSE, and MAE are widely used as performance metrics in Section 4.

5 Limitations / Open Research Areas

5.1 Load Forecasting

In [7], if the number of decision trees increases in RF, it causes overﬁtting problem.

RF does not perform well if the number of trees is higher than 128. More trees in RF

require more computational resources. SMA predicts load on previous trends. Al-

though, there is more volatility (abrupt changes) in short time frames as compared to

long time frames. So, it does not show good results in short time frame. In [3], sea-

sonal parameter is directly passed to SVR for training. As we know, load increases in

summer and winter due to excessive use of air conditioning and electric heat appli-

ances. So, there are massive spikes in dataset during summer and winter that affects

the accuracy and gives unfortunate result. It is not very practical to handle extensive

scale data. In [9], Load demand data is decomposed by EMD that is transferred to

DBN. DBN composed of two Restricted Boltzmann Machines’ (RBMs) and one

Electricity Load and Price Forecasting using Machine Learning Algorithms . 11

ANN is applied for load demand forecasting. Although because of its longer com-

putational time some optimisation technique can be applied to increase efﬁciency of

deep learning model.

In [10], EMD is used to denoise input electrical load signal. This processed data

is then transferred to mixed-Extreme Learning Machine (ELM) model for forecast-

ing. Mixed kernel combines RBF kernel and UKF kernel. However, EMD has mode

mixing problem. Extreme Learning Machine (ELM) performs forecasting in one

go as it is single FFNN, though, it cannot tune parameters effectively. In [11], ap-

ply Discrete Wavelet Transform (DWT) to solve mode mixing problem of EMD

method. It has somewhat solved this issue; however, it takes longer compression

time. Incremental learning of RVFL model is applied to update itself whenever new

input patterns are available. Weights are updated on basis of its error rate. RVFL

has efﬁcient approximation ability despite this; it takes longer computational time.

In [12], we may combine short term with medium-term to detect market demand

shrinking information problem. We also optime the weights to improve forecasting

accuracy by using optimizing approach. In [13], we may use advance models to se-

lect best inputs for electricity load forecasting, and some other inﬂuential factors can

also be added in hybrid model as future work. PSR-BSK model proposed in paper

[14] can also be applied in natural gas forecasting to accurate demand forecasting

results for decision-makers.

5.2 Price Forecasting

In [17], the aim of authors, is to select the best input against desired output. How-

ever, they do not prove that the model selects the best input. The computational

time of the model is very high as compared to benchmarks. In this paper, model

does not give accurate results in summer, autumn and spring due to high volatility

in price. If steps are less than 10 in multi-step ahead forecasting, then it shows good

results; otherwise, it gives poor results. In [18], when data will be available in pub-

lic sources for market participants, many other inﬂuencing factors can also use to

improve the accuracy of price forecasting. In [19], the proposed model can enhance

with further four advance deep learning models like autoencoders to check the ef-

fect of forecasting accuracy. The individual benchmark combine with expert advice

can also be studied. The effect of model can also be analysed by using a large num-

ber of experiments. In [20], model shows less accuracy on ﬁnal prices. The model

fails to produce price peaks as high as in reality. It only shows the liability of price

increases.

The applied ANN is calibrated on historical data. If there is any change in mar-

ket design or introduce large amount of new capacity then model should be adjusted

and recalibrated. In [21], for tuning parameters, the authors used Differential Evo-

lution(DE)algorithm. However, DE is prone to converging to local optima and do

not cover the entire search space, the computational time and complexity of the

system model are high as the Random Forest (RF) take much time and harder to

12 Arooj et al.

construct the decision tree. The prediction accuracy is low for RF. The RF when

taking garbage input,its prediction accuracy decreases. In [22], the proposed model

could also investigate on large datasets and see the impact on MAPE values.

6 Conclusion

Load and price forecasting shows future trends. Accurate electricity forecasting is

the key for the secureness of grid. In this paper author focus on literature in last

few years. This paper concerns to provide a review on forecasting in terms of price

and load with different ML approaches. The author classiﬁes the papers regarding

problems, solutions, constraints, and future challenges.The objective of this paper

to provide a survey on price and load and compare their evaluation metrics to check

which approach gives best solution. For future, survey of different deep learning

approaches like CNN, ResNet, LSTM and their computational time need to be con-

sidered, though computational time is critical in deep learning applications.

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