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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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