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Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid

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Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter's data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue. A good forecasting model makes an acceptable use of all characteristics of the electric loads' data and also reduces dimensionality of that data. Many data-driven methods have been proposed in the literature for load forecasting. In this paper, EEMD based ECNN model is proposed to forecast load of electic-ity using AEMO data. From the results, ECNN outperforms benchmark methods especially by applying EEMD for decomposition and DAE for feature extraction.
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Short-Term Load Forecasting using
EEMD-DAE with Enhanced CNN in Smart Grid
Afrah Naeem, Hira Gul, Arooj Arif, Sahiba Fareed, Mubbashra Anwar, and
Nadeem Javaid
Abstract Traditional grid moves toward Smart Grid (SG). In traditional grids, elec-
tricity was wasted in generation-transmission-distribution. SG is introduced to solve
prior issues. In smart grids, how to utilize massive smart meter’s data in order to im-
prove and promote the efficiency and viability of both generation and demand side
is a compelling issue. A good forecasting model makes an acceptable use of all char-
acteristics of the electric loads’ data and also reduces dimensionality of that data.
Many data-driven methods have been proposed in the literature for load forecasting.
In this paper, EEMD based ECNN model is proposed to forecast load of electic-
ity using AEMO data. From the results, ECNN outperforms benchmark methods
especially by applying EEMD for decomposition and DAE for feature extraction.
1 Introduction
A huge amount of electricity consumption data has been collected due to preva-
lent popularity of smart grids. Smart grid combines information and communication
technology in the existing energy grids to provide optimum power with remarkable
efficiency by interchanging the real time data between consumers and suppliers. For
the time being, the liberalism of power industry on the consumer side, has vastly
grown worldwide. How to utilize massive data of smart meters in order to improve
and promote the efficiency and viability of both generation and demand side is a
compelling issue [1]. Short-term load forecasting plays a vital role in energy distri-
bution and management of modern power system, as it is necessary to ensure the
balance between generation and demand. Also, accurate load forecasting becomes
an important issue with the increased complexity of smart grids. Due to imprecise
forecasting, its effect in the planning and scheduling of demand are drastic. For
Afrah Naeem, Hira Gul, Arooj Arif, Sahiba Fareed, Mubbashra Anwar, and Nadeem Javaid (Cor-
responding Author)
COMSATS University Islamabad, email: nadeemjavaidqau@gmail.com
1
2 Afrah et al.
example, an underestimation of electricity results in power outage whereas overes-
timation drives the units to be wasted causing excessive energy supply [2]. A good
forecasting model makes an acceptable use of all characteristics of the electric loads
data and also reduces dimensionality of that data. In other words, the goal of electric
load forecasting is to ensure stable use of supply while keeping its operational cost
as low as possible [3].
Electricity load forecasting can be categorized into four main types summarized
as follows: Very Short-Term Load Forecasting (VSTLF) ranges from few minutes
to hours, Short-Term Load Forecasting (STLF) performs hours or days prediction,
Medium-Term Load Forecasting (MTLF) falls between weeks or months whereas
Long-Term Load Forecasting (LTLF) lies between years. STLF aims to assure relia-
bility of the electric power system and adjust it to prevent from loss of power failure
and overloading. In the last decades, STLF algorithms have been widely studied [4].
In this paper, we focus on day-ahead load forecasting i.e. STLF.
Since 1940s, numerous statistical based linear time series forecasting approaches
have been proposed whose shared goal is to practice time series analysis for deduc-
ing the future energy requirement. The most acknowledged methods are based on
Auto Regressive Integrated Moving Average (ARIMA) [5], linear regression [6]
and Kalman filtering [7] etc. These statistical models acts as a baseline for electric
load forecasting nowadays [8]. At present, traditional methods are developed, the-
ories become mature and approaches are simple, which have numerous composite
advantages. These traditional methods do not perform well when dealing with non-
linear data as they carry out linear based analysis and establish quantitative relation
between data.
The non-linearity of time series data can be handled with the decomposition tech-
niques. EMD is frequently applied to remove noisy part of load signals in the pre-
processing phase of short-term load forecasting. However, important issues with
EMD are end data points extending and mode mixing problem that needs to be
considered [9]. In other words, EMD lacks its mathematical foundation. In [10],
characteristic waves were used to solve this problem. Besides this, it may devi-
ate from actual output. In [11, 12], authors proposed mirror extending method to
help resolve end data points extending difficulty. End mirror extending for high fre-
quency and least square polynomial for low frequency were used. In [13], authors
combined EMD with Multiplex Overlap Discrete Wavelet Transform (MODWT)
that tried to overcome this problem. MODWT applied low and high pass filters to
the input which is then transferred to EMD to further denoise data. Although, this
creates computational complexity. So, there is room for improving solutions for end
point problem of EMD method.
Some artificial intelligence based methods like ANN, deep neural network and
SVR have been proposed with improved efficiency by utilizing more refined fea-
tures. However, these features are directly given into model for training without
separate pre-processing. Thus, we expect to build a model that separately performs
pre-processing and feature engineering in order to ensure performance with well-
defined features. Extreme Learning Machine (ELM) performs forecasting in one
go, though, it is highly sensitive to initial parameters. It reduces computational time
Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid 3
however, it cannot update biases, weights and parameters. Radial Basis Function
(RBF) kernel used in ELM also assigns same weights to all attributes because they
are treated equally in distance computation. Also, EMD based ELM gives less ac-
curate results for the large dataset [14].
This paper intents to propose a model for electricity forecasting using ECNN:
DAE is first used to perform feature extraction then ECNN is used to predict load.
The proposed model is then compared with the performance of two different meth-
ods to see which technique gives best results. The limitations and future challenges
are also identified for further research.
The remaining paper is structured as follows: in Section II, the problems of each
paper are discussed critically. Section III introduces a model proposed. Section IV
describes the data and forecasting results. Limitations and future research challenges
are discussed in Section V.
2 Literature survey
In paper [14], authors considered a problem that it is tough for an individual model
to perform well in all cases; most probably when dealing with non-linear data. One
kernel function may not capture all properties of data. When dealing with Extreme
Learning Machine (ELM), the choice of kernel is most serious problem. They pro-
posed a model in which to reduce noise, EMD is used that diminish noise inference
of original load data. That denoised data is then decomposed into training and test-
ing set. The processed data is added to mixed kernel-based ELM model to perform
forecasting. Mixed kernel is composed of RBF kernel and UKF kernel function. If
forecasting results are greater than accuracy limitation then it gets final results, oth-
erwise goes back to EMD and repeats the whole process. The proposed model is first
compared with three methods: RBF-ELM, UKF-ELM and Mixed-ELM. Then com-
pared with three benchmark methods: MFES, ESPLLSVM and combined method.
First three performed well at some specific time than proposed method. Whereas, on
comparison with benchmark methods, the proposed method has improved electricity
load forecasting accuracy undoubtedly. Four performance metrics are chosen to per-
form simulation, which are: RMSE, MAPE, MAE and AE. Each of them evaluate
the methods from different facets. Whereas, AE is neglected as it contains negative
values. ELM performs forecasting in one go, though, it is highly sensitive to initial
parameters. It reduces computational time however, it cannot update biases, weights
and parameters. EMD based ELM gives less accurate results for large dataset.
In paper [15], authors examined a problem that an important challenge with EMD
is end data points extending problem that needs to be considered. In [10], character-
istic waves were used to solve this problem. Beside this, it may deviate from actual
output. Also, the weights in single feedforward neural network are conventionally
tuned by BP algorithm. BP has gradient descent problem i.e. trapped in local min-
ima. It is also time consuming due to large number of iterations. The proposed model
is comprised of two phases: pre-training phase and incremental learning phase. In
4 Afrah et al.
pre-training, MODWT is applied to decompose signal into several IMFs and one
residue. The training vector is then passed to RVFL network and trained for each
obtained sub-series. A new input matrix is constructed that combines three aspects:
original time series data, temperature data and prediction results of all sub-series. In
incremental learning phase, this matrix is passed so new weight matrix can be con-
structed. The testing data is used to check the error level. If error decreases than cur-
rent error level, updates remain kept otherwise weight matrix is not changed. Two
error measures are considered to validate accuracy of forecasting model: RMSE
and MAPE. The proposed method is compared with eight benchmark learning algo-
rithms: GLMLF-B, SLFN, RVFL, RF, EMD-RF, EMD-SLFN, persistence method
and EMD-RVFL. The critical distance calculated is 3.1. Despite of RVFL has great
approximation ability and efficient generalization performance although, when it
comes to deep learning, there is tradeoff between functional links and number of
hidden neurons in RVFL. The advantages of functional links decrease as number of
hidden neurons increase in order to train model sufficiently [15, 16]. DWT is used
to analyze non-linear and non-stationary data. However, this technique is based on
assumption that processed data is linear and stationary. It is highly flexible; wavelet
functions can be openly chosen. It also allows good localization in time domain.
Despite this, it takes longer compression time. In paper [16, 17], EMD is used to
denoise data. Also, one classifier is used to deal with linear and non-linear series i.e.
IMFs and residue.
In paper [18], authors addressed a problem that original non-linear time series
data often lost some useful information due to dissonance; it contains some noisy
part which affects the forecasting results. ‘A divide and conquer’ approach i.e. EMD
is used that decomposes load demand data into several IMFs and one residue. One
training matrix is then constructed for IMFs and residue which is then passed as
input to DBN. DBN is trained to obtain predicted results. By using summation or
linear neural network, prediction results of DBN are then combined to devise an
ensemble output for time series. Performance comparison for day-ahead and half-
an-hour load forecasting is conducted. Two error measures are considered to check
precision of forecasting model: RMSE and MAPE. The proposed method is com-
pared with nine benchmark algorithms: persistence method, ANN, SVR, RF, DBN,
EDBN, EMD-RF, EMD-SVR and EMD-ANN. The critical distance calculated is
3.0.
In paper [19], problem was addressed i.e. the neural network-related algorithms
are mostly practiced to express non-linear forecasting relationship. They have solved
underfitting problem successfully, although, overfitting is a major disadvantage of
neural networks when it comes to large number of hidden nodes. Box-Cox nor-
malization and copula analysis were applied in data preprocessing. A forecasting
based on DBN was constructed to predict the power load. A comparison analysis
between SVR, NN, classical DBN, R-DNN, SOM-SVM and ELM was conducted.
Performance of proposed model was evaluated using day-ahead and week-ahead
peak load forecasting using three performance metrics namely MAPE, RMSE and
Hit Rate (HR). Backpropagation was used for fine tuning of parameters. Although,
it may trap in local optima while search which then increase computational time
Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid 5
and affect performance accuracy of the model. In both papers, DBN was used for
forecasting electricity load. However, major drawback of DBN is that it may lead to
overfitting problem when performing feature engineering step.
Another paper [20], addressed a problem that due to increase in market compe-
tition, more active and unpredictable electricity market, renewable requirements of
integration, shareholders are more interested in some alternative to point forecasting
techniques. Also, most common technique used in area of daily load forecasting is
deep learning theory. Still, there are two common issues about the theory; overfit-
ting and more computation time consumption. To solve this, the improved WNN
and generalized ELM are used in probabilistic load forecasting problem. Uncertain-
ties of noise in data are considered which is a consequence of non-iterative training
machine. A combined GELM, wavelet preprocessing and bootstrapping model is
proposed. Two tests; Friedman and post-hoc tests are used to validate the approach.
Four performance indicators are used for error measures, Prediction Intervals’ Nom-
inal Confidence (PINC), Prediction Intervals Coverage Probability (PICP), ECR and
Average Coverage Error (ACE). The proposed model is then compared with four
methods: RELM/Bootstrapping, BP/Bootstrapping, Quantile Regression and Dol-
phin/Bootstrapping. GELM was applied as SLFN, so it cannot encode more than
one layer of abstraction, it also has overfitting troubles. It has fast training time but
slow evaluation speed.
In paper [7], authors addressed pain in the neck that by using only individual
time series model, we cannot predict good results. A combination or hybrid of two
models is more effective than individual model. In addition. EMD has an end effect.
An improved EMD (IEMD) was used for reducing information by decomposing sig-
nals into residual term and non-linear series with calendar effects. ARIMA model
was used for the first segment as it performs well with the linear data. WNN was
then used to capture calendar effects i.e. non-linear series. At last, Fruit Fly Opti-
mization Algorithm (FOA) was used for optimization. To make a fair comparison,
four performance metrics were used as measurement index; MAPE, MAE, MPE
and RMSE. Load data was collected from AEMO and New York market. For com-
parison, schemed model was compared with two methods: WTNNEA, WGMIPSO.
Paper [21] addressed a problem that, most researches only focused on one-step load
forecasting and also ignore time feature that is crucial for performing accurate fore-
casting. For this, a Multi Scale Convolutional Network (MS-CNN) was proposed
to extract high-level features. For strengthning periodical description and encode
load data for improving prediction, the periodical coding strategy was designed. In
addition, the deficiencies of traditional time coding approaches were evaluated. Au-
thors also proposed TCMS-CNN which optimizes the structure of CNN and raise
the accuracy of multi-step load forecasting by extracting more relationships with
periodical characters. Three error measures were chosen to evaluate performance:
RMSE, MAE and MAPE. To verify MS-CNN, it was compared with some popular
models; LSTM, SVR and ResNet on one step prediction. From the results, it was
concluded that TCMS-CNN was improved by 3.54. For direct multi step load fore-
casting, the performance of TCMS-CNN was not satisfied. Its network structure is
also complex that cause longer execution time for training and inference process.
6 Afrah et al.
In paper [22], authors focused on issue that single artificial intelligence mod-
els that deals with non-linear patterns may stuck in local optima and also failed to
achieve satisfactory performance. Authors proposed hybrid model, that first decom-
pose raw wind speed series and send it to LSTM which acts as predictor. RELM
improves performance of model significantly. At last, IEWT is employed for outlier
detection and correction of the forecasting series. Four performance evaluation in-
dexes are chosen: the MAE, MAPE, RMSE and SDE. The proposed hybrid model
has high computational time. Even if LSTM is an improved gate version RNN, it has
still some limitations. It has probability of loosing important information in training
process. Also, it does not possess the ability to perform parallel processing, which
results in more time consumption and wastage of resources. The problem addressed
in this [23] is that, in order to save power energy and logically schedule electric gen-
erator units, it is necessary to forecast day-ahead overall load of electricity. Three
kinds of data were collected for solve problem encountered: historical load data,
weather and season parameters. SDA was used for feature extraction because of its
ability to adapt numerical inputs and impressiveness. SDA was composed of sub-
networks which receives data collected and extract some high-level features. The
refined one were then fed into SVR to forecast electricity load. This paper used
MAPE as performance indicator to evaluate error measures. Proposed model was
compared with SVR and ANN method. SVR has good regularization capability and
can easily handle non-linear data. It also deals with classification and regression
problem instead feature scaling must be done before applying SVM, which is not
carried out. SVR has long training time. Also, algorithmic complexity and memory
requirements of SVM are very high. One key issue addressed in this [24] paper is
that it is important for stable power supply to predict electric load accurately. By
using conventional statistical methods, it is not easy to predict exact power con-
sumption for short-term load forecasting for university buildings due to their differ-
ent usage patterns. For this, the data collected serve as input variable for forecasting
model. In pre-processing, temperature data is adjusted. The processed data is passed
to simple moving average method which is used to smoothen short-term fluctuations
and highlight long-term trends for time-series data. RF is used to perform predic-
tion which yields improved performance than SVR and ANN because of conducting
Time Series Cross Validation (TSCV). To evaluate performance of forecast model,
two performance metrics were chosen; RMSE and MAE. Finally, performance is
evaluated using TSCV. The moving average method used predicts future demand
based on assumption of past fluctuations or trends. However, in case if assumption
is not valid, this method gives poor result.
In [25], authors highlighted a problem that Demand Response (DR) aims at im-
proving the efficiency of smart grids. One key issue of DR is the calculation of load
baselines. The aim of this paper was to propose a generic method to forecast a fair
baseline in DR programs. A historical data of office buildings was used to determine
optimal functional relationship between inputs and train SVR model to predict out-
come. Dry bulb temperature was used as input to model in order to improve accuracy
of SVR. The baseline was calculated up to 8 hours. The gap between baseline and
actual load was calculated during DR event. AE, Mean Error (ME) and MAE were
Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid 7
taken as evaluation standards during a response time. Comparison was done with
eight existing methods for four office buildings. SVR model is computationally de-
manding and it requires a lot of training data. In addition, it also suffers from the
curse of dimensionality.
Paper [26] focuses on problem that due to volatility and stochasticity of loads
data, load forecasting for an individual household is much more serious problem.
The sparse code features are used to improve the accuracy of individual meter load
forecasting. Error analysis is done on MAPE error distribution of different meters.
The next-day and next-week total load forecasting is done and evaluated using er-
ror measures i.e. MAE, RMSE and MAPE. In paper [27], addressed a problem
that some of the traditional forecasting models ignores the necessity of data pre-
processing. Also, limitations of individual model are often ignored, which leads to
highly inaccurate prediction. Data was first de-noised using decomposition and en-
semble technique. A novel deciding weight method based on a swarm intelligence
and the leave-one-out strategy was developed to combine the individual models. The
accuracy of short-term wind speed was improved based on combined novel model.
Three performance metrics were used to evaluate combined novel model: RMSE,
MAE and Sum of Error Square (SSE). The Diebold–Mariano (DM) test was used
to evaluate and verify the performance of proposed combined model. Although the
proposed model had high computational time. Some more work using artificial in-
telligence based methods like ANN and deep neural network have been done in
literature [28]-[34].
3 Methodology
In this paper, EEMD based ECNN is proposed to forecast electricity load as shown
in Figure 1. The applied approach consists of the following steps:
1. The time series data is first normalized using min-max normalization and stan-
dardization (or Z-score normalization).
2. After normalization, input data is then transferred to EEMD. EEMD decom-
poses data into several IMFs and one residue.
3. For further denoising that data and performing feature extraction, we construct
a training matrix for each IMFs and one residue and pass it to DAE.
4. This denoised data is then transferred to ECNN to formulate final load fore-
casting results.
3.1 Dataset
The electricity load data sets from Australian Energy Market Operator (AEMO)
[35] are collected for comparison. AEMO is an electric power and gas industry. It
generates, transmits and distributes electricity to all Australians. It generates a large
8 Afrah et al.
Table 1: Overview of Related Work
Techniques Objectives Data source/ achievements Limitations
EMD mixed kernel-based ELM [1] Electric load forecasting
Improved accuracyby using EMD, RBF and
UKF kernel function. AEMO data collected
EMD may cause mode mixing problem and elm
cannot tune parameters, bias and weights
EMD based Ensemble DBN [2] Short-term and very short-term forecasting
Efficiently extractfeatures in an
unsupervised way and better performance for
half-an-hour ahead forecasting. AEMO data collected
Time consuming, EMD has end points extendingproblem
MODWT-EMD-RVFL[3] Short-term load forecasting Betterperformance in terms of
accuracy,efficiency. AEMO data collected
RVFLtradeoffs between functional links and number of hidden neurons
SDA-SVR[4] Day-ahead load forecasting Mainlyfocus on feature
extraction to extract well refined features
DWT takeslonger compression time
SMA-RF-CV [6] Short-term load forecasting Loadprediction for university building with acceptable accuracy long training time and feature scaling not done
IWNN-GELM [7] Probabilistic load forecasting Better accuracy and reliability,beat benchmark methods Simple moving averagegives poor result in case of false assumption
TCMS-CNN [8] Multi-step short-term load forecasting Time coding wasconsidered GELM cannot encode more than one layer of abstraction, overfitting troubles
CM-DBN [21] Short-term power load
Predicts the power load using
day-ahead, week-ahead forecasting
and week-ahead peak load forecasting
the performance of TCMS-CNN was not satisfied,
network structure is complex and time consuming
IEMD-ARIMA-WNN-FOA [22] Short-term load Better accuracy BP may trap in local optima
IEMD-SVR [23] Short-term load Calculate load demand baselines,
take ambient temperature of two hours as input
High computational time
ICEEMDAN-GWOcombined model [24] Short-term wind speed Improve the accuracyof individual meter load forecasting Computationally demanding, requires a lot of training data
EWT-LSTM-RELM-IEWT[25] Short-termwind speed Improved performance More computation time, lose important information in the training process
amount of data about electricity load, its price, demand, generation and distribution,
etc. Load data of 2018 is taken from AEMO, which is used to train and test the
proposed method.
3.2 Pre-processing
Load data collected from AEMO is first normalized using min-max normalization
and then standardization normalization. Min-max rescales the range of data [0,1].
Standardization sets mean of data to 0 and standard deviation to 1. It gives equal con-
sideration for each feature. For regression data, standardization improves numerical
stability of model and may speed up training process [36].
3.3 Noise reduction
EEMD is used to reduce noise inference of original load signal and decompose data
into several IMFs and one residue. A practical approach to obtain swift frequency
data from non-linear and non-stationary datasets. IMF function has mean value of
Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid 9
zero, along with one extreme between two modes [14]. After EEMD, decomposed
data is then passed to feature engineering phase.
3.4 Feature Extraction
Feature extraction is used to extract some features; it creates a new, smaller set of
features that still captures useful information in order to give more accurate results
than original data. In literature, different techniques are used for feature extraction.
DAE is used for feature extraction in the proposed model. During training, loss
function is defined similar to Root Mean Square Error (RMSE) that was defined
previously in convolutional autoencoder. At every iteration of training, network will
compute a loss between noisy data output by decoder and ground truth (de-noisy
data). It also tried to minimize loss or difference between reconstructed data and
original noise-free data.
3.5 ECNN
We propose Enhanced version of Convolutional Neural Network (ECNN), in which
additional hidden layers are added. In order to avoid overfitting problem and in-
crease performance of ECNN, parameters are adjusted. ECNN is composed of two
parts; in first part, ECNN uses hidden layers to learn the given input features with
weight sharing property. Whereas in the second part, ECNN flattens the output of
above layers and perform forecasting. In ECNN, one convolution layer with sixty-
Fig. 1: Proposed Model
four filters of two by two matrix is added using relu as activation function. After
convolution layer, two dense layers are added. Dense layer represents a multiplica-
tion vector matrix and the values in the matrix are trainable parameters which get
updated during backpropagation. After dense layer, a dropout layer appear which is
used for regularization to prevent overfitting problem. During each iteration of train-
ing process, a neuron is temporarily discarded with some certain probability. The
10 Afrah et al.
reason behind dropout is to prevent the network being dependent on a small num-
ber of neurons and force every neuron to operate independently which increases
the accuracy of ECNN. Lastly, two Max Pooling layers are added which reduce
the number of parameters, computation time, dimensionality, and controlling over-
fitting problem by reducing the dimentional size of the network. In Max Pooling
layer, at each iteration, maximum number of parameters are taken out and the rest
are dropped, which both compresses the training time as well as combats overfitting.
In flatten layer, output of layers is flattened as discussed above to deliver into the
next fully connected layers to perform forecasting.
4 Simulation results
For all-time series data sets, data is linearly scaled between [0,1]. To perform simu-
Fig. 2: Results of ECNN compared with CNN
lation, toolbox named as deep learning was used for neural network. The implemen-
tation of DBN, EMD mixed kernel-based ELM and proposed EMD based ECNN is
performed in Python. There are two types of performance matrix that are used for
error measurement of proposed model i.e. RMSE and MAPE.
Short-Term Load Forecasting using EEMD-DAE with Enhanced CNN in Smart Grid 11
The values on x-axis of EMD based DBN and ELM are the number of observa-
tions performed which are then compared with actual dataset. On one hand, DBN
was applied on subset of actual dataset. Activation function chosen for DBN is lin-
ear. On y-axis electricity power (MW) is plotted. MAPE value of DBN computed
Fig. 3: Results of ECNN compared with DBN
is 2.67 and RMSE is 1.626. Also, MAPE value of EMD mixed kernel-based ELM
is 3.87 whereas RMSE is 0.4.ELM on the other hand is applied for comparison with
proposed model. ELM is a single feedforward neural network.ELM does not fully
capture values from dataset; it does not cover up whole search space for forecasting
on basis of features extracted. From the results, it is clear that ECNN outperforms
benchmark methods.
12 Afrah et al.
Fig. 4: Results of MLP
5 Conclusion
Accurate electricity forecasting is essential for the stability of grid. ML is a well-
known technique that is used for electricity forecasting. This paper proposed a
model for electricity load forecasting using EEMD-DAE-ECNN. The performance
is then compared with three methods: DBN, ELM and original CNN. From the re-
sults, it has been seen that ECNN outperforms benchmark methods especially by
applying EEMD for decomposition and DAE for feature extraction.
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... DL models can automate feature engineering, handle large and complex data [46,47,48]. Therefore, these models widely used in recent studies [49,50,51]. In [26,45], authors have proposed HNN for ET detection such as LSTM-MLP, W&D-CNN. ...
... from tabulate import tabulate 47 48 Read the Dataset from Google Drive as shown below, data.csv file is in 50 data folder 51 52 df = pd . read_csv ( '/ content / drive / My Drive / data / data . ...
Thesis
Electricity theft (ET) is a major problem in developing countries. It a�ects the economy that causes revenue loss. It also decreases the reliability and stability of electricity utilities. Due to these losses, the quality of supply e�ects and tari � imposed on legitimate consumers. ET is an essential part of Non-technical loss (NTL) and it is challenging for electricity utilities to �nd the responsible people. Several methodologies have developed to identify ET behaviors automatically. However, these approaches mainly assess records of consumers' electricity usage, may prove inadequate in detecting ET due to a variety of theft attacks and irregularity of consumers' behavior. Moreover, some important challenges are needed to be addressed. (i) The number of normal consumers has been wrongly identi�ed as fraudulent. This leads to high False-positive rate (FPR). After the detection of theft, on-site inspection is needed to validate the detected person, either is it fraudulent or not and it is costly. (ii) The imbalanced nature of datasets which negatively a�ect on the model's performance. (iii) The problem of over�tting and generalization error is often faced in deep learning models, predicts unseen data inaccurately. So, the motivation for this work to detect illegal consumers accurately. We have proposed four Arti�cial intelligence (AI) models in this thesis. In system model 1, we have proposed Enhanced arti�cial neural network blocks with skip connections (EANNBS). It makes training easier, reduces over�tting, FPR and generalization error, as well as execution time. Temporal convolutional network with enhanced multi-layer perceptron (TCN-EMLP) is proposed in system model 2. It analyzes the sequential data based on daily electricity-usage records, obtained from smart meters. At the same time, EMLP integrates the non-sequential auxiliary data, such as data related to electrical connection type, property area, electrical appliances usage, etc. System model 3 based on Residual network (RN) that is used to automate feature extraction while three tree-based classi�ers such as Decision tree (DT), Random forest (RF) and Adaptive boosting (AdaBoost) are trained on the obtained features for classi�cation. Hyperparameter tuning toolkit is presented in this system model, named as Hyperactive optimization toolkit. Bayesian is used as an optimizer in this toolkit that aims to simplify the tuning process of DT, RF and AdaBoost. In system model 4, input is forwarded to three di�erent and well-known Machine learning (ML) techniques, i.e., Support vector machine (SVM), as an input. At this stage, a meta-heuristic algorithm named Simulated annealing (SA) is employed to acquire optimal values for ML models' hyperparameters. Finally, ML models' outputs are used as features for meta-classi�ers to achieve �nal classi�cation with Light Gradient boosting machine (LGBM) and Multi-layer perceptron (MLP). Furthermore, Pakistan residential electricity consumption dataset (PRECON1), State grid corporation of china (SGCC2) and Commission for energy regulation (CER3) datasets is used in this thesis. SGCC dataset contains 9% fraudulent consumers, which are extremely less than non-fraudulent consumers, due to the imbalance nature of data. Furthermore, many classi�cation techniques have poor predictive class accuracy for the positive class. These techniques mainly focus on minimizing the error rate while ignoring the minority class. Many re-sampling techniques are used in literature to adjust the class ratio; however, sometimes, these techniques remove the important information that is necessary to learn the model and cause over�tting. By using six previous theft attacks, we generate theft cases to mimic the real world theft attacks in original data. We have proposed the combination of oversampling and under-sampling techniques that is Near miss borderline synthetic minority oversampling technique (NMB-SMOTE), Tomek link borderline synthetic minority oversampling technique with support vector machine (TBSSVM) and Synthetic minority oversampling technique with near miss (SMOTE-NM) to handle imbalanced classi�cation problem. We have conducted a comprehensive experiment using SGCC, CER and PRECON datasets. The performance of suggested model is validated using di�erent performance metrics that are derived from Confusion matrix (CM).
... Liu et al. [15] also find that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al. [90] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals. These modes and residuals are passed onto a Denoising Auto Encoder (DAE) for feature extraction. ...
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The increased digitalisation and monitoring of the energy system opens up numerous opportunities to decarbonise the energy system. Applications on low voltage, local networks, such as community energy markets and smart storage will facilitate decarbonisation, but they will require advanced control and management. Reliable forecasting will be a necessary component of many of these systems to anticipate key features and uncertainties. Despite this urgent need, there has not yet been an extensive investigation into the current state-of-the-art of low voltage level forecasts, other than at the smart meter level. This paper aims to provide a comprehensive overview of the landscape, current approaches, core applications, challenges and recommendations. Another aim of this paper is to facilitate the continued improvement and advancement in this area. To this end, the paper also surveys some of the most relevant and promising trends. It establishes an open, community-driven list of the known low voltage level open datasets to encourage further research and development.
... Liu et al.[143] also nd that a sparse encoding network can improve the forecast for an LSTM at the household-level. Naeem et al.[164] develop a day-ahead load forecast of an Australian network-grid using an Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into Intrinsic Mode Functions (IMF) and residuals. These modes and residuals are passed onto a Denoising Auto Encoder (DAE) for feature extraction. ...
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