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Wind Power Forecasting Based on Efficient Deep Convolution Neural Networks

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Due to the depletion of fossil fuel and global warming, the incorporation of alternative low carbon emission energy generation becomes crucial for energy systems. The wind power is a popular energy source because of its environmental and economic benefits. However, the uncertainty of wind power, makes its incorporation in energy systems really difficult. To mitigate the risk of demand-supply imbalance by wind power, an accurate estimation of wind power is essential. Recognizing this challenging task, an efficient deep learning based prediction model is proposed for wind power forecasting. In this proposed model, Wavelet Packet Transform (WPT) is used to decompose the wind power signals. Along with decomposed signals and lagged inputs, multiple exogenous inputs (calendar variable, Numerical Weather Prediction (NWP)) are used as input to forecast wind power. Efficient Deep Convolution Neural Network (EDCNN) is employed to forecast wind power. The proposed model's performance is evaluated on real data of Maine wind farm ISO NE, USA.
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Wind Power Forecasting Based
on Efficient Deep Convolution
Neural Networks
Sana Mujeeb1, Nadeem Javaid1(B
),HiraGul
1, Nazia Daood1,
Shaista Shabbir2, and Arooj Arif1
1COMSATS University Islamabad, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Virtual University, Kotli 11100, Pakistan
http://www.njavaid.com/
Abstract. Due to the depletion of fossil fuel and global warming,
the incorporation of alternative low carbon emission energy generation
becomes crucial for energy systems. The wind power is a popular energy
source because of its environmental and economic benefits. However, the
uncertainty of wind power, makes its incorporation in energy systems
really difficult. To mitigate the risk of demand-supply imbalance by wind
power, an accurate estimation of wind power is essential. Recognizing
this challenging task, an efficient deep learning based prediction model
is proposed for wind power forecasting. In this proposed model, Wavelet
Packet Transform (WPT) is used to decompose the wind power signals.
Along with decomposed signals and lagged inputs, multiple exogenous
inputs (calendar variable, Numerical Weather Prediction (NWP)) are
used as input to forecast wind power. Efficient Deep Convolution Neural
Network (EDCNN) is employed to forecast wind power. The proposed
model’s performance is evaluated on real data of Maine wind farm ISO
NE, USA.
Keywords: Data analytics ·Wind power ·Demand side
management ·Energy management ·Forecasting ·Deep learning
1 Introduction
Due to the continual decrease in fossil fuel, the energy crisis has become crucial
[1]. To mitigate the energy crisis, regulative acts that encourage the utiliza-
tion of renewable energy are promoted worldwide. Among the renewable energy
resources, wind energy, as an alternative to traditional generation, has attracted
a lot of attention. The reason of popularity of wind power is its environment
friendly nature. Wind power has no carbon emission and therefore, helps in
reducing environmental pollution [2]. It is introduced worldwide as a way to
reduce greenhouse gas emission. According to the Global Wind Energy Council
[3], the cumulative capacity of wind power reached 486 GW across the global
c
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): 3PGCIC 2019, LNNS 96, pp. 47–56, 2020.
https://doi.org/10.1007/978-3-030-33509-0_5
48 S. Mujeeb et al.
market in 2016. Wind power is expected to significantly expand leading to an
overall zero emission power system.
The wind power is not only environmental friendly, but it also has a low
investment cost (due to the developing technology) [4]. In the USA, the U.S.
Department of Energy target of renewable integration is responsible of providing
20% of the total energy through wind, by the year 2030 [5]. In this regard, the
Independent System Operators (ISOs) are producing significant wind power and
increasing their wind generation.
It is acknowledged widely that accurate WPF significantly reduces the risks
of incorporating wind power in power supply systems [6]. Generally, the WPF
results are in the deterministic form (i.e., point forecast). Reducing the fore-
casting errors of WPF is the focus of many researchers [7]. A point forecast is
the estimated value of future wind energy. However, the wind power is random
variable having a Probability Density Function (PDF), and point forecasts is
unable to capture the uncertainty of this random variable. This the limitation of
the point forecasts. Therefore, point forecasts have limited use in stability and
security analysis of power systems. To overcome the limitation of point forecasts,
deep learning methods are widely used in the field of WPF and other electricity
related forecasting tasks [810]. Deep Neural Networks (DNN) have the inherent
property of automatic modeling of the wind power characteristics [11].
The wind power has a chaotic nature. Therefore, the incorporation of wind
power in power supply systems is a risky task. To mitigate this risk, wind power
forecasting is the most popular method. The wind power is forecasted using clas-
sical [917], statistical and artificial intelligent methods. In literature, there are
two types of wind power forecasting techniques: time series [12] and multivari-
ate [13]. The accuracy of wind power forecasting is very important to avoid the
demand supply imbalance. Therefore, researcher are still competing to improve
the wind power forecasting accuracy.
Convolution Neural Network (CNN) is a state-of-the-art deep learning
method. It is the CNN’s characteristic that it can extract the spatial features
automatically. CNN is the most popular method for extracting features from
the images and widely used in the field of computer vision. The efficient feature
extraction capability of CNN motivate us to exploit it for wind power forecast-
ing. CNN successfully extracts the spatio-temporal correlations in wind power
data.
2 Contributions
In this paper, we are concerned with the problem of predicting the wind power.
The contributions of this research work are listed below:
For forecasting wind power, the Numeric Weather Prediction (NWP) are used
along with lagged wind power and Wavelet Packet decomposed (WPT) past
wind.
Efficient Deep Convolution Neural Networks Based Wind Power Forecasting 49
Fig. 1. Overview of proposed system for wind power forecasting.
A predictive Deep Convolution Neural Network (DCNN) model for accurate
wind power prediction is proposed, that employs an efficient activation func-
tion and loss function in output layer (Fig.1).
3 Proposed Model
The proposed method for forecasting wind power generation and power man-
agement algorithm are discussed in this section.
3.1 Data Preprocess
The features and targets (wind power) are normalized using min-max normal-
ization (as shown in Eq. 1).
Xnor =Ximin(X)
max(X)min(X)(1)
The inputs to the forecast model are shown in Table 1. Three types of inputs are
given to the forecasting model are: (i) Numerical Weather Prediction (NWP):
dew point temperature, dry bulb temperature, wind speed, (ii) past lagged values
of wind power and (iii) wavelet packet decomposed wind power. The wavelet
decomposition is described in the next section.
3.2 Feature Engineering
The historical wind power signal is decomposed using Wavelet Packet Trans-
form (WPT). The WPT is a general form of the wavelet decomposition which
50 S. Mujeeb et al.
Table 1. Inputs to the forecast model.
Input Description
Dew point temperature Past NWP forecast
Dry bulb temperature Past NWP forecast
Wind speed Past NWP forecast
Lagged wind power 1 Wind power (t-24)
Lagged wind power 2 Wind power (t-25)
Decomposed wind power Wavelet decomposed past wind power
Hour Time of the day
Fig. 2. Wavelet packet tree with three levels.
performs a better signal analysis. WPT was introduced in 1992 by Coifman and
Wickerhauser [14]. Unlike, Discrete Wavelet Transform (DWT), the WPT wave-
forms or packets that are interpreted by three different parameters: frequency,
position and scale (similar to the DWT). For every orthogonal wavelet func-
tion multiple wavelet packets are generated, having different bases (as shown in
Fig. 2). With the help of these bases, the input signal can be encoded in such a
way that the global energy of signal is preserved and exact signal can be recon-
structed effectively. Multiple expansions of an input signal be achieved using
WPT. The suitable most decomposition is selected by calculating the entropy
(e.g. Shannon entropy). The minimal representation of the relevant data based
on a cost function is calculated in WPT. The benefit of WPT is its characteristic
of analyzing signal in different temporal as well as spatial positions. For highly
nonlinear and oscillating signal like wind power DWT doesn’t guarantee good
results [15]. In WPT, both the approximation and detail coefficients are further
decomposed into approximation and detail coefficients as the level of tree goes
deeper. Wavelet packet decomposition operation can be expressed Eqs. 2and 3.
Efficient Deep Convolution Neural Networks Based Wind Power Forecasting 51
For a signal ato be decomposed, two filters of size 2Nare applied on a.The
corresponding wavelets are h(n) and g(n).
W2n(a)=2
2N1
k=0
h(k)Wn(2ak) (2)
W2n+1(a)=2
2N1
k=0
g(k)Wn(2ak) (3)
Where, the scaling factor W0(a)=φ(a) and the wavelet function is W1(a)=
ψ(a).
After decomposing the past wind signals, the engineered features along with
NWP variables and lagged wind power are input to the proposed forecasting
model. The proposed forecasting model is discussed in the next section.
3.3 Efficient DCNN
The inputs are given to the Efficient Deep CNN (EDCNN) for predicting day-
ahead hourly wind power (24 values). Firstly, the functionality of trivial CNN is
discussed in this section. Secondly, the proposed method EDCNN is explained.
CNN works on the principle of visual system of human brain. CNN has an
excellent capability of extracting deep underlying features from data. The CNN
effectively identify the spatially local correlations in data through convolution
operation. In the convolution operation, a filter is applied to a block of spatially
adjacent neurons and result is passed through an activation function. This output
of convolution layer becomes the input to next layer’s neuron. Thus, the input to
every neuron of a layer is the output of convolved block of previous layer. Unlike
ANN, the CNN training is efficient due to the weight sharing scheme. Due to
the weight sharing, the learning efficiency improves. CNN is composed of three
altering layers: (i) convolution layer, (ii) sampling layer and (iii) fully connected
layer. The convolution operation can be explained by following Eq. 4.
Suppose, X = [x1,x
2,x
3,...,x
n] are the training samples and C =
[c1,c
2,c
3,...,c
n] is the vector of corresponding targets. nis the number of train-
ing samples. CNN attempts to learn the optimal filter weights and biases that
minimize the forecasting error. CNN can be defined as:
Ym
i=f(wmXm
i+bm) (4)
Where, i = [1, 2, ..., n] and m = [1, 2, ..., M]. mis the number of layer to be
learned. The filter weights of the mth layer is denoted by wm.bmrepresents the
corresponding biases, refers to the convolution operation. f(·) is the nonlinear
activation function. Ym
iis the feature map generated by sample Xiat layer m.
The proposed forecasting method EDCNN, there are eleven layers: three con-
volution layers, three max pooling layers, two batch normalization layers, three
ReLU (Rectified Linear Unit) layers, one modified fully connected layer and
52 S. Mujeeb et al.
modified output layer (Enhanced Regression Output Layer (EROL)). Function-
ality of two layers are modified, in order to improve the forecasting performance
of EDCNN.
According to the ANN literature, there is no standard way to choose an
optimal activation function. However, its a well-known fact that machine learning
methods have an excellent optimization capability of any model or function.
On basis of these facts, a modified activation function is employed in a hidden
layer. The proposed activation function is ensemble of three activation functions:
hyperbolic tangent, sigmoid and radial base function.
TH =exex
ex+ex(5)
σ=ex
1+ex(6)
φ(x, c)=φxc(7)
F=(TH +σ+φ)
3(8)
In the proposed output layer EROL, a modified objective function is embedded.
The objective is to minimize the absolute percentage error between the forecasted
values and actual targets. The objective can be expressed as 9:
min Loss =L(w, Xi,c
i)+F(Yk,c) (9)
Where, L(w, Xi,c
i) is the forecasting error or loss from sample Xi,andF(Yk,c)
represents the objective. The input to objective function F(·) are the feature
maps generated at the kth layer and care their respective targets. The objective
function is expressed as 10:
F=1
n
n
i=1
Yici
Yi
100 (10)
0 50 100 150
Time (H)
0
200
400
600
800
Wind Power (MW)
Spring
0 50 100 150
Time (H)
0
200
400
600
Wind Power (MW)
Summer
0 50 100 150
Time (H)
0
200
400
600
Wind Power (MW)
Autumn
0 50 100 150
Time (H)
0
500
1000
Wind Power (MW)
Winter
Fig. 3. Wind power of all four seasons of a year.
Efficient Deep Convolution Neural Networks Based Wind Power Forecasting 53
Where, Yiis the output at the output layer and ciis the desired or actual target.
4 Results and Analysis
The proposed algorithms are implemented using MATLAB R2018a on a PC with
core i3 processor and 4 GB RAM.
4.1 Data Description
The three year hourly data of wind power is taken from ISO New England’s
wind farm located in Maine [16]. The weather parameter, i.e., wind speed data
is taken from Maine weather station data repository.
4.2 Wind Power Analysis
The wind power is directly proportional to the wind speed. The wind speed vary
from season to season. In Maine, USA the wind speed is effected by seasonality.
In Fig. 3, the one day wind power of all the four seasons, is shown. The wind
power in autumn is higher compared to other seasons. The reason behind this is
the fast winds in coastal area of Maine, where the wind turbines are installed.
4.3 Performance Evaluation
For performance evaluation of wind power forecasting, three evaluation indica-
tors are used: MAE and NRMSE and MAPE (Fig. 4and Table 2).
0 5 10 15 20 25
Time (H)
100
200
300
400
500
Wind Power (MW)
Spring
Observed
ECNN
SELU CNN
CNN
0 5 10 15 20 25
Time (H)
200
400
600
800
Wind Power (MW)
Summer
Observed
ECNN
SELU CNN
CNN
0 5 10 15 20 25
Time (H)
600
700
800
900
1000
Wind Power (MW)
Autumn
Observed
ECNN
SELU CNN
CNN
0 5 10 15 20 25
Time (H)
0
200
400
600
Wind Power (MW)
Winter
Observed
ECNN
SELU CNN
CNN
Fig. 4. All seasons predictions of wind power.
54 S. Mujeeb et al.
Table 2. MAPE and NRMSE of proposed and compared methods.
Method Season MAPE NRMSE MAE
CNN Spring 8.42 2.34 3.34
Summer 8.23 2.27 3.24
Autumn 7.9 2.65 3.36
Winter 8.1 2.71 2.89
SELU CNN Spring 3.47 0.12 3.1
Summer 3.62 0.13 3.3
Autumn 3.45 0.12 3.4
Winter 3.27 0.17 3.2
EDCNN Spring 2.67 0.092 2.4
Summer 2.43 0.096 2.24
Autumn 2.56 0.085 2.67
Winter 2.62 0.094 2.18
Table 3. Diebold-Mariano test results at a 95% confidence level.
Method Season Diebold-Mariano
EDCNN vs. CNN Spring
Summer
Autumn
Winter
EDCNN vs. SELU CNN Spring
Summer
Autumn -
Winter
4.4 Diebold-Mariano Test
The aforementioned error indicator are utilized for accuracy comparison of fore-
casting models. However, the lesser error or higher accuracy of a model doesn’t
guarantee its superiority over other models. A model is better as compared to
another model, if the difference between their accuracies is statistically signif-
icant. Different statistical tests are used to validate the significance of models,
such as Friedman test [17], error analysis [18], Diebold-Mariano (DM) test [19],
etc. To validate performance of the proposed forecasting model EDCNN, a well-
known statistical test DM is used. Diebold and Mariano propose the classical
Diebold-Mariano statistical test in 1995 [19]. DM is widely used for validation
of wind power forecasting [20].
A vector of values that are to be forecasted are [y1,y
2, ..., y
n]. These values
are predicted by two forecasting models: M1and M2. The forecasting errors of
these models are [εM1
1
M1
2, ..., ε
M1
n] and [εM2
1
M2
2, ..., ε
M2
n]. A covariance
Efficient Deep Convolution Neural Networks Based Wind Power Forecasting 55
loss function L() and differential loss are calculated in DM as 11 [21]:
dM1,M
2
t=L(εM1
t)L(εM2
t) (11)
DM is applied to the forecasting results of EDCNN and two compared methods:
CNN and SELU CNN [13]. The results of DM test with confidence level of 95%
are shown in Table 3. The check marks are shown at the places where the perfor-
mance of EDCNN is significantly better as compared to the comparable method.
If the forecasting accuracy is not significantly improved, hyphen is placed. The
performance of proposed forecaster EDCNN is compared with standard CNN
and SELU CNN [13]. The predictive analysis are performed for all four seasons
of a year.
5 Conclusion
In this paper, the problem of predicting wind power generation is considered. In
order to take part in the daily market that regulates the supply and demand in
the Maine electricity system. A deep-learning technique EDCNN is developed
to accurately predict the hourly day-ahead wind power on the Maine wind farm
data. The numeric results validates the efficiency of proposed model for wind
power forecasting.
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The revolution of power grids from traditional grids to Smart Grids (SGs) requires effective Demand Side Management (DSM) and reliable Renewable Energy Sources (RESs) incorporation in order to maintain demand, supply balance and optimize energy in an environment friendly manner. Data analytics provide solutions to the emerging challenges of power systems, such as DSM, environmental pollution (due to carbon emission), fossil fuel dependency mitigation, RESs incorporation, cost curtailment, grid’s stability and security. To efficiently manage electricity and maximize the profit of power utilities several tasks are focused in this thesis, i.e., prediction of electricity load to avoid demand and generation mismatch, wind power forecasting to satisfy energy demand effectively, electricity price forecasting for regulating market operations, carbon emissions forecasting for reducing payment of carbon tax, Electricity Theft Detection (ETD) for recovering power utilities’ revenue loss caused by electricity theft. In addition to that, a wind power forecast based DSM scheme is proposed. Furthermore, impact of RESs integration level on carbon emissions, electricity price and consumption cost is quantified. Both forecasting and classification techniques are utilized for efficient energy management. Forecasting of electricity load, price, wind power and carbon emissions is performed, whereas, classification of fair and fraudulent electricity consumers is performed. To balance electricity demand and supply, electricity load forecasting is required. Three models are proposed for this purpose, i.e., Deep Long Short-Term Memory (DLSTM), Efficient Sparse Autoencoder Nonlinear Autoregressive eXogenous network (ESAENARX) and Differential Evolution Recurrent Extreme Learning Machine (DE-RELM). DLSTM utilizes univariate data and gives single result, whereas, ESAENARX and DE-RELM model multivariate data and predict electricity load and price simultaneously. Due to adaptive and automatic feature learning mechanism, DLSTM achieves accurate results for separate forecasting of electricity load and price. ESAENARX and DE-RELM models are enhanced by newly proposed efficient feature extractor and model’s parameter tuning, respectively. Real-world datasets of ISO-NE, PJM, NYISO are used for load and price forecasting. The purpose of regulating the electricity market operations is achieved by forecasting of electricity load, price, wind power and carbon emissions. Wind power generation is predicted by an efficient model named Efficient Deep Convolution Neural Network (EDCNN). Moreover, a DSM strategy is also proposed based on predicted wind power generation. Power utilities have to pay carbon emissions tax imposed by government. To pay less carbon emissions tax, carbon emissions prediction is required, which helps in encouraging electricity consumers to shift their consumption load to low carbon price time periods of the day. For accomplishing the carbon emissions forecasting task, an efficient model named as Improved Particle Swarm Optimization based Deep Neural Network (IPSO DNN) is proposed. This model is improved by tunning the parameters of DNN by newly proposed improved optimization technique named as IPSO. ISO-NE dataset is used for wind power and carbon emissions forecasting. To reduce the financial loss of power utilities ETD is very important. For this purpose four models are proposed, named as, Differential Evolution Random Under Sampling Boosting (DE-RUSBoost), Jaya-RUSBoost, RUS Ensemble CNN (RUSE-CNN) and anomaly detection based ETD. In DE-RUSBoost and Jaya-RUSBoost, the parameters of RUSBoost classifier are tunned by DE and Jaya optimization techniques, respectively. In RUSE-CNN, RUS data balancing technique is applied along with ensemble CNN to improve ETD performance. DE-RUSBoost, Jaya-RUSBoost and RUSE-CNN are supervised model that work on labeled electricity theft data. Whereas, anomaly detection based ETD model is capable of identifying electricity theft from unlabeled electricity consumption data. Real-world datasets of SGCC, UMass, PRECON, CER, EnerNOC and LCL are used for ETD. Simulation results show that all the proposed models perform significantly better on real-world dataset as compared to their state-of-the-art counterpart models. The improved feature engineering and model hyper-parameter tuning enhance the performance of the proposed models in terms of prediction and classification results.
... CNN based models have gained attention in speech recognition, object detection, video processing and image classification due to their state-of-the-art performance [131,132,133]. Researchers have been using a variant of CNN model, known as the TCN model, for sequence modeling activities. ...
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).
... Wind power has attracted a lot of a attention as a RES, recently. Wind power has gained popularity due to its characteristics of: wide availability, low investment cost [67,68] and no carbon emissions. Wind power helps in reducing environmental pollution [69]. ...
Thesis
Full-text available
The revolution of power grids from traditional grids to Smart Grids (SGs) requires effective Demand Side Management (DSM) and reliable Renewable Energy Sources (RESs) incorporation in order to maintain demand, supply balance and optimize energy in an environment friendly manner. Data analytics provide solutions to the emerging challenges of power systems, such as DSM, environmental pollution (due to carbon emission), fossil fuel dependency mitigation, RESs incorporation, cost curtailment, grid’s stability and security. To efficiently manage electricity and maximize the profit of power utilities several tasks are focused in this thesis, i.e., prediction of electricity load to avoid demand and generation mismatch, wind power forecasting to satisfy energy demand effectively, electricity price forecasting for regulating market operations, carbon emissions forecasting for reducing payment of carbon tax, Electricity Theft Detection (ETD) for recovering power utilities’ revenue loss caused by electricity theft. In addition to that, a wind power forecast based DSM scheme is proposed. Furthermore, impact of RESs integration level on carbon emissions, electricity price and consumption cost is quantified. Both forecasting and classification techniques are utilized for efficient energy management. Forecasting of electricity load, price, wind power and carbon emissions is performed, whereas, classification of fair and fraudulent electricity consumers is performed. To balance electricity demand and supply, electricity load forecasting is required. Three models are proposed for this purpose, i.e., Deep Long Short-Term Memory (DLSTM), Efficient Sparse Autoencoder Nonlinear Autoregressive eXogenous network (ESAENARX) and Differential Evolution Recurrent Extreme Learning Machine (DE-RELM). DLSTM utilizes univariate data and gives single result, whereas, ESAENARX and DE-RELM model multivariate data and predict electricity load and price simultaneously. Due to adaptive and automatic feature learning mechanism, DLSTM achieves accurate results for separate forecasting of electricity load and price. ESAENARX and DE-RELM models are enhanced by newly proposed efficient feature extractor and model’s parameter tuning, respectively. Real-world datasets of ISO-NE, PJM, NYISO are used for load and price forecasting. The purpose of regulating the electricity market operations is achieved by forecasting of electricity load, price, wind power and carbon emissions. Wind power generation is predicted by an efficient model named Efficient Deep Convolution Neural Network (EDCNN). Moreover, a DSM strategy is also proposed based on predicted wind power generation. Power utilities have to pay carbon emissions tax imposed by government. To pay less carbon emissions tax, carbon emissions prediction is required, which helps in encouraging electricity consumers to shift their consumption load to low carbon price time periods of the day. For accomplishing the carbon emissions forecasting task, an efficient model named as Improved Particle Swarm Optimization based Deep Neural Network (IPSO DNN) is proposed. This model is improved by tunning the parameters of DNN by newly proposed improved optimization technique named as IPSO. ISO-NE dataset is used for wind power and carbon emissions forecasting. To reduce the financial loss of power utilities ETD is very important. For this purpose four models are proposed, named as, Differential Evolution Random Under Sampling Boosting (DE-RUSBoost), Jaya-RUSBoost, RUS Ensemble CNN (RUSE-CNN) and anomly detection based ETD. In DE-RUSBoost and Jaya-RUSBoost, the parameters of RUSBoost classifier are tunned by DE and Jaya optimization techniques, respectively. In RUSE-CNN, RUS data balancing technique is applied along with ensemble CNN to improve ETD performance. DE-RUSBoost, Jaya- RUSBoost and RUSE-CNN are supervised model that work on labeled electricity theft data. Whereas, anomaly detection based ETD model is capable of identifying electricity theft from unlabeled electricity consumption data. Real-world datasets of SGCC, UMass*, PRECON, CER, EnerNOC and LCL are used for ETD. Simulation results show that all the proposed models perform significantly better on real-world dataset as compared to their state-of-the-art counterpart models. The improved feature engineering and model hyper-parameter tuning enhance the performance of the proposed models in terms of prediction and classification results.
... [56]- [61] and generation [62]. In [45], authors used Restricted Boltzman Machine (RBM) with pre-training and Rectified Linear Unit (ReLU) to forecast day and week ahead load. ...
Research Proposal
Full-text available
With the advent of Smart Grid (SG), the data collected by smart meters and Phasor Measurement Units (PMUs) has become a valuable source for grid operators and researchers to perform advanced analytics. In SG, the energy-related data is collected in a very huge volume, at a high velocity from a variety of sources. Data analytics provide solutions to the emerging challenges of power systems, such as: Demand Side Management (DSM), environmental pollution (due to carbon emission), fossil fuel dependency mitigation, reliable Renewable Energy Sources (RESs) incorporation, cost curtailment, grid’s stability and security. The global energy demand is increasing with the increasing population. The trivial power generation source, i.e., fossil fuel is decreasing continuously. Moreover, the environmental pollution is increasing at an alarming rate due to the carbon emission for trivial power generation sources. Therefore, effective DSM and RES incorporation have become important to maintain demand, supply balance and optimize energy in an environment friendly manner. DSM programs are based on the future energy consumption and price predictions. On the other hand, the reliable incorporation of RES is possible if there is a correct estimation of future generation. For this purpose, Deep Learning (DL) combined with data analytics techniques are proposed in this research. The aim of this research is to explore the SG databases and device solutions to the aforementioned problems. First, the predictive modeling is used for learning the consumption pattern from the data, to ensure the uninterrupted power supply. Predictive analytics are performed on energy price that is beneficial in effective DSM programs’ formulation. Moreover, as a popular RES, the wind power is analyzed and predicted. A DSM algorithm is proposed that considers the day-ahead energy price, consumption and wind power forecast for energy demand management. This research applies the data science techniques to the smart grid data as well as elaborates the benefits of this emerging data to the smart grid.
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