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An Efficient CNN and KNN Data Analytics for Electricity Load Forecasting in the Smart Grid

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The significant part of smart grid is to make smart grid cost-efficient by predicting electricity price and load. To improve the prediction performance we proposed an Efficient Convolutional Neural Network (ECNN) and Efficient K-nearest Neighbour (EKNN) in which the parameters are tuned. It may be difficult to deal with huge amount of load data that is coming from the electricity market. To overcome this issue, we incorporated three modules in the proposed methodology. The proposed model consists of feature engineering and classification. Feature engineering is a two-step process (feature selection and feature extraction); for the purpose of feature selection Mutual Information (MI) is used which reduces the redundancy among features and for feature extraction Recursive Feature Elimination (RFE) is used to extract the principle features from the selected features and reduces the dimensionality of features. Finally, after training the data-set and the removal of the duplicate features load prediction is done by ECNN and EKNN. The ECNN and EKNN outperforms better then traditional Convolutional Neural Network (CNN) and K-nearest Neighbour (KNN). The forecast performance is evaluated by comparing the results with MAPE, RMSE, MAE and MSE. i.e. 10.8, 7.5, 7.15, and 10.4 respectively.
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An Efficient CNN and KNN Data
Analytics for Electricity Load Forecasting
in the Smart Grid
Syeda Aimal1, Nadeem Javaid1(B
), Tahir Islam1, Wazir Zada Khan2,
Mohammed Y. Aalsalem2, and Hassan Sajjad1
1COMSATS University, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Farasan Networking Research Laboratory,
Department of Computer Science and Information System, Jazan University,
Jazan 82822-6694, Saudi Arabia
http://www.njavaid.com
Abstract. The significant part of smart grid is to make smart grid
cost-efficient by predicting electricity price and load. To improve the
prediction performance we proposed an Efficient Convolutional Neural
Network (ECNN) and Efficient K-nearest Neighbour (EKNN) in which
the parameters are tuned. It may be difficult to deal with huge amount
of load data that is coming from the electricity market. To overcome
this issue, we incorporated three modules in the proposed methodology.
The proposed model consists of feature engineering and classification.
Feature engineering is a two-step process (feature selection and feature
extraction); for the purpose of feature selection Mutual Information (MI)
is used which reduces the redundancy among features and for feature
extraction Recursive Feature Elimination (RFE) is used to extract the
principle features from the selected features and reduces the dimension-
ality of features. Finally, after training the data-set and the removal of
the duplicate features load prediction is done by ECNN and EKNN. The
ECNN and EKNN outperforms better then traditional Convolutional
Neural Network (CNN) and K-nearest Neighbour (KNN). The forecast
performance is evaluated by comparing the results with MAPE, RMSE,
MAE and MSE. i.e. 10.8, 7.5, 7.15, and 10.4 respectively.
1 Introduction
Nowadays data analytic plays significant role in every field. Smart grids intro-
duced an advance informational infrastructure that allows optimization in energy
production, transmission, distribution and storage. Smart grid collects data from
different resources and stores it using data analytic techniques. In Smart grid,
bi-directional communication generates data having huge volume, variety and
velocity. Advance data analytic techniques are needed to figure out the required
information from the available raw data. Machine learning techniques are one
c
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https://doi.org/10.1007/978-3-030-15035-8_57
Electricity Load Forecasting 593
of the reliable methods for predicting load and price. Supervised learning algo-
rithms are the part of machine learning which are used for predictions. Cate-
gories of machine learning algorithms are classification and regression. KNN and
CNN are the part of supervised learning algorithm. Deep learning is a learning
method for multiple levels of abstraction and representation which contains an
input layer, hidden layer, and an output layer. Deep learning comes under an
umbrella of machine learning which is based on learning levels of representation,
corresponding to a hierarchy of features, factors or concepts, where the concept
of higher-level is derived from lower-level and vice versa.
The recent advancements in sensor based technologies hold the promise of
novelty in energy consumption models that can better characterize the underly-
ing patterns. Demand-side Management (DSM) is the main and essential com-
ponent towards energy efficiency and management. DSM in smart homes and
residential buildings is a key component towards sustainability and efficiency in
urban environment.
Smart grid is an electrical grid which consists of range of energy and opera-
tional measures. The main objective of grid is to scale back electricity peak load
and to attain the balance between power supply and demand. Customers are
encouraged to partake within the operations of smart grid, the cost of electric
load will be reduced by the preservation of electricity and by shifting the load
towards off-peak hours.
1.1 Motivation or Problem Statement
In paper [1], the authors forecasted electricity price with deep learning
approaches and empirical comparison of traditional algorithms. Similarly, in
[3], the authors tried to forecast price. However, the load is neglected. Authors
also worked on peak load reduction. However, the accuracy of load and price is
neglected.
1.2 Contribution
In this paper, We have proposed new classification techniques (i.e. ECNN and
EKNN). The terms forecasting and prediction are used alternatively.
Load is predicted using the new classifiers which beats the traditional tech-
niques in terms of accuracy. Prediction is done on NYISO Data-set. We incorpo-
rated three modules in the proposed methodology. The proposed model consists
of feature engineering and classification. Feature engineering is a two-step pro-
cess (feature selection and feature extraction); for the purpose of feature selec-
tion Mutual Information (MI) is used which reduces the redundancy among fea-
tures and for feature extraction Recursive Feature Elimination (RFE) is used to
extract the principle features from the selected features and reduces the dimen-
sionality of features. Finally, after training the data-set and the removal of the
duplicate features load prediction is done by ECNN and EKNN. The ECNN and
EKNN outperforms better then traditional Convolutional Neural Network and
K-nearest Neighbour.
594 S. Aimal et al.
The main contributions are summarized as follow:
The accuracy must be improved.
Reduce the computational time.
Less resources are utilized.
Enhance the classifier to increase the prediction accuracy.
The rest of this paper is organized in the following way:
In Sect. 2, the related work in context of forecasting load and price is
reviewed. In Sect. 3, the system model is explained. In Sect. 4, the simulations
are performed in python environment on intel core i5, 8 GB RAM, and 1 TB
hard disk. In Sect. 5, the result of this paper is concluded.
2 Related Work
In this section, we have discussed different proposed schemes and techniques for
price and load forecasting [1]. The accuracy in forecasting helps the energy sup-
pliers to optimize their strategies for gaining profit. Likewise, to gain a successful
trade in electricity market different techniques have been proposed by different
authors. Short-term Price Forecasting (STPF) and Short-term Load Forecasting
(STLF) play principle role in the management of energy and resource trading
in the grids [2]. Similarly, Auto-regressive Integrated Moving Average (ARIMA)
performs good in stable electricity market.
In the electricity market, load and price forecasting are essential for optimal
operation planning; most of the load and price forecasting methodologies suffer
from lack of efficient feature selection techniques. To overcome this issue, the
authors in [3] proposed a new hybrid filter-wrapper approach that selects the
most relevant features according to the costumer input in a coordinated manner.
In [4], to construct a forecasting model authors presented a hybrid algorithm
which concatenate Empirical Mode Decomposition (EMD), Similar Days (SD)
Selection, and Long Short-term Memory (LSTM) neural network. Moreover,
to evaluate the accuracy of model between the forecasting and historical data
extreme gradient boosting-based weighted k-means algorithm is adopted.
The authors also deal with two types of forecasting online and day-ahead
hourly forecasting [5]. Stacked Denoising Auto-encoder (SDA) models are com-
pared with data-driven approaches in online forecasting whereas in day-ahead
forecasting, the accuracy and effectiveness is further validated. Mathematical
and statistical models like ARIMA, SARIMA etc., belongs to the classical type
of classifiers whereas artificially intelligent models are CNN, LSTM [6] (Table 1).
The authors in [7], proposed an Improved Elman Neural Network (IENN)
based forecast engine to forecast the load. The weights for this prediction model
carried out with an intelligent algorithm to find better forecasting results.
The efficiency of this model is brought-out to the real world engineering in
the comparison with other forecasting models [8]. In [9], the authors focused
on coordinated management of renewable and traditional energy in which the
Electricity Load Forecasting 595
Table 1. Summary of related work
Technique Objective Data Set Limitations
RBM, Relu Load PJM Overfitting problem
SNN, DNN Price AEMO Cannot deal with
non-linearity
RNN, LSTM Load IRISH Time complexity
CNN, LSTM Price International
Exchange
Overfitting problem,
Computationally expensive
C4.5, ID3 Price prediction,
C4.5 beats ID3 in
decision tree
building
Redundant feature not
removed
Hilbertian
ARMAX
Price EPEX Cannot deal with extreme
non linearity
MLR, NN Load ISONE Not suitable for long term
forecasting
MI, ANN Load PJM Time complexity
LSTM, DNN,
GRU
Load and price EPEX Over fitting
DRN Load and
temperature
ISONE Over fitting
ANN, SVM,
RBFNN
Load and price NSW Hard to tune parameters
DWT-IR,
SVM, Sperm
Whale
Load and price NYISO Slow convergence
STLF, STPF Load and weather PJM, NYISO Computationally expensive
QO-ABC,
LS-SVM
Load and price NYISO, PJM Time complexity
DNN Load and price EPEX Space complexity
DNN, LSTM Price and load
prediction
NYISO Redundancy features not
removed
authors formulate the problem as a three-stage Stackel-berg game in which the
authors employ backward induction and nash equilibrium method.
The authors also proposed unsupervised data clustering and frequent pattern
mining analysis on energy time series [10], and Bayesian network [10] prediction
for energy usage. The authors also perform experiments using real-world smart
meter data-sets. Accuracy in prediction is a considerable issue with multiple
data [11] which are applicable for a specified time frame. For each day ahead
coming-up with, the operations and the facility from the wind resources can be
foreseen. Observations and a series of models that span the temporal and spacial
596 S. Aimal et al.
scales of the matter; correct prediction could be a massive knowledge downside
that needs disparate knowledge. In [12], multiple models that are applicable for
a particular time-frame, and application of artificial intelligence techniques [13]
are studied.
In paper [14], the authors propose a new prediction methodology that makes
an attempt to generalize the quality of ARMAX statistical model to pair of
metric space. The structure of this planned model is a statistical regression where
parameters operate variables. The variables are lagged values of the series. i.e.
past determined innovations (like moving average terms), or exogenous variables.
In paper [15], a prediction strategy is planned for electricity markets. The
strategy uses large amount of data with hourly information; which is further
used as an input for two separate prediction models. Moreover, a rolling horizon
framework is planned to supply correct updates for the predictions.
The model in paper [16] is intended to find the incidence of abnormal events
and conditions. A generative model for anomaly detection is taken into consider-
ation and the hierarchical data-set of the network is collected from smart meters.
We tend to additionally address three challenges existing in smart grid:
1. large-scale variable count measurements
2. missing points
3. variable choice
In [17], the value prediction is entirely viewed as a symbol process. Data
integrity in [18], the attacks are expected to damage the performance of predic-
tion systems, which can have a significant impact on power systems and therefore
the resilience of power grids.
It reveals the result of knowledge integrity attacks on the accuracy of four repre-
sentative load forecasting models (multiple regression towards the mean, support
vector regression, artificial neural networks, and fuzzy interaction regression).
In paper [19], the authors tend to propose a probabilistic data-driven model
for consumption prediction in residential buildings. The model relies on theorem
network framework that is ready to find dependency relations between conduc-
tive variables which are able to relax the assumptions that are usually created
in ancient prediction models.
3SystemModel
In the proposed approach, we enquire the problem of electricity load prediction.
In our prediction model, we tunned the hyper-parameters of KNN. In addition
to this, we also enhanced CNN in which we used two MAX-pooling and three
convolutional layers. Dense and flatten are used as a fully connected layers.
Historical load data that is taken from grid (NYISO) is used as a training data-
set for EKNN and ECNN; we used 80% of data for training purpose and rest of
20% is used for testing (Fig. 1).
Electricity Load Forecasting 597
Fig. 1. Prediction model
The proposed approach depicts that model is made up of three parts:
1. Feature selection
2. Feature extraction
3. Classification
Feature engineering which is two-step process (selection and extraction), and
then classification. In our prediction model, we first took the real data-set of
one year (i.e. january 2016–2017) from NYISO. The data is hourly-base which
is further utilize for the purpose of training and testing.
Design Goal: The goal of this model is to predict the load accurately and
efficiently. For this purpose, the preprocessed data is used on which feature
engineering process and classification is done.
To overcome these constraints:
The data sampling must be done for the reduction of consumption time.
Data formatting must be done carefully.
Redundant data must be removed.
3.1 The Following Metrics Are the Most Essential Metrics
for Accessing the Performance of the Model
Proposed model that is applied for the purpose of predicting load must work
efficiently.
Accuracy of classification can be influenced directly by the performance of
feature engineering.
Forecasted accuracy is highly dependent on the goal of model.
598 S. Aimal et al.
Algorithm 1. Prediction Algorithm
Input : Load Data
/* Separating target from features */
X : features of data
Y : target data
xtrain, x test, y train, y test = split(x,y);
combine imp = MI-imp + RFE-imp;
MI imp = importance calculate by MI;
selected feature = RFE(5, x train, y train);
IF
Combine imp of MI threshold And RFE is true, then
Reserve feature;
ELSE
Drop feature;
ENDIF
Prediction using EKNN and ECNN with tuned parameters;
Compare predictions with y test
3.1.1 Feature Selector
In the feature selection, the importance of features can be generated. In our
model we first set a certain threshold value i.e. 0.5 for the selection of important
features. In our model, MI is used for feature selection.
Feature selection is used for the following top reasons:
It allows us to reduce over-fitting.
The accuracy of a model can be improved.
The complexity of a model can be improved.
It increases the efficiency of machine learning algorithm to train faster.
3.1.2 Feature Extractor
When it comes to feature extraction, it is different from feature selection. After
the process of feature selection, feature extraction is done which down-sample
the most principle features. In our case, we extracted five principle features by
using RFE.
It is used to further improve the quality, speed, and effectiveness of supervised
learning.
It is a reduction process.
it is used for the transformation of attributes.
3.1.3 Classifier
In Classification, we discover a new set of observations on the basis of a trained
set of data. For load prediction, we used ECNN and EKNN as a classifiers.
Electricity Load Forecasting 599
Algorithm 2. Algorithm of KNN
Input
Training data is loaded
Choose from the feature F
for Each point in data: do
Find Euclidean distance
Euclidean distance is stored in sorted list
The first k point is choosen
Assign a class on the basis of majority of classes
end forR(f-i + 1) - f*
End
Algorithm 3. Hyperparameter Optimization Algorithm
Input
Start procedure
Choose from the feature-set F
for Trai n N e two r k do
ModelEstimation
end forReturnBestHyperparameters( )
End
4 Simulation and Reasoning
In this section, we are going to explain the following:
Setup.
Results.
Evaluation Performance.
Each of the points mentioned above are describe in detail below.
4.1 Simulation Setup
To simulate our proposed model, we performed the simulation in python envi-
ronment. The simulation is performed on system with specification intel core i5,
8 GB RAM, and 1 TB hard disk. One year hourly electricity load data of NYISO
is used for the evaluation of proposed scheme.
4.2 Simulation Results
Features are selected with MI.
Principle Components are extracted with RFE.
Actual load is compared with ECNN and EKNN.
The performance is compared with four performance evaluators i.e. Mean
Average Error (MAE), Mean Square Error (MSE), Root Mean Square Error
(RMSE), and Mean Absolute Percentage Error (MAPE).
600 S. Aimal et al.
Fig. 2. Importance feature selection with MI
Fig. 3. Feature extraction with RFE
Electricity Load Forecasting 601
Fig. 4. Load predicted by ECNN and EKNN
MI Technique is applied to select the importance of features with respect to
the target on the basis of a certain threshold value (i.e. 0.5) (Fig.2). Features
which have little impact on load are dropped from data as shown in Fig. 4.The
most five principle feature are extracted to reduce the features dimensionally
through RFE as shown in Fig. 3. ECNN and EKNN as a classifier outperforms
best at the forecasting accuracy of electric load as shown in Fig. 4. The accuracy
that is achieved by ECNN is 92% and the accuracy that is achieved by EKNN
is 93%. The ECNN and EKNN is close to the actual load which is good result.
4.3 Performance Evaluation
In order to investigate the robustness and accuracy of the proposed model; a
comparison is conducted as shown in Fig. 5. For performance evaluation, four
means are used. i.e. MAE, MSE, RMSE, and MAPE. MAPE whose error values
are i.e. 10.847888888888887, 7.566138821530217, 7.155807083333333, and 10.424
respectively.
MAP E =1
T
TM
tm=1
|AvFv
Av
|∗100 (1)
RM SE =
1
T
TM
tm=1
(AvFv)2(2)
MSE =1
T
TM
tm=1
(AvFv)2(3)
MAE =N
n=1 |(FvAv)|
N(4)
602 S. Aimal et al.
Fig. 5. ECNN performance evaluation metrics
5 Conclusions
In this paper, the problem of electricity load forecast is taken into consideration.
The data is obtained from grid (NYISO data-set) for one year (i.e. january 2016
to 2017). We took required data from the grid which is hourly-base data that
is further utilize for the purpose of training and testing. The process of feature
engineering is done in which the important features are selected using MI. After
the selection of important features, RFE is used for extracting five principle
features.
The trained data-set is further used for load prediction that is done by our
proposed classifiers (ECNN accuracy is 92% and for EKNN accuracy is 93%).
The predicted electricity load outperforms better then other classifiers in terms
of accuracy. The forecast performance is evaluated by comparing the results with
MAPE, RMSE, MAE and MSE. i.e. 10.8,7.5, 7.15, and 10.4 respectively.
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... On the other hand, CNN (Convolutional Neural networks) outperforms SVM as will be shown in our proposed model. Syeda et al. [22] published their recent study about predicting power price and load using smart grid. Their research uses feature engineering to overcome the problem of load parameters and in order to use Efficient Convolutional Neural Network (ECNN) and Efficient K-nearest Neighbour (EKNN) for prediction. ...
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