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Big Data Analytics based Short Term Electricity Load Forecasting Model for Residential Buildings in Smart Grids

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Electricity load forecasting has always been a significant part of the smart grid. It ensures sustainability and helps utilities to take cost-efficient measures for power system planning and operation. Conventional methods for load forecasting cannot handle huge data that has a nonlinear relationship with load power. Hence an integrated approach is needed that adopts a coordinating procedure between different modules of electricity load forecasting. We develop a novel electricity load forecasting architecture that integrates three modules, namely data selection, extraction, and classification into a single model. First, essential features are selected with the help of random forest and re-cursive feature elimination methods. This helps reduce feature redundancy and hence computational overhead for the next two modules. Second, dimensionality reduction is realized with the help of a t-stochastic neighbourhood embedding algorithm for the best feature extraction. Finally, the electricity load is forecasted with the help of a deep neural network (DNN). To improve the learning trend and computational efficiency, we employ a grid search algorithm for tuning the critical parameters of the DNN. Simulation results confirm that the proposed model achieves higher accuracy when compared to the standard DNN.
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Big Data Analytics based Short Term Electricity
Load Forecasting Model for Residential Buildings
in Smart Grids
Inam Ullah Khan1,, Nadeem Javaid2, C. James Taylor1, Kelum A. A. Gamage3and Xiandong Ma1
1Engineering Department, Lancaster University, Bailrigg, Lancaster LA1 4YW, UK
2Department of Computer Science, COMSATS University Islamabad, Islamabad, 44000, Pakistan
3School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
*Correspondence: i.u.khan@lancaster.ac.uk
Abstract—Electricity load forecasting has always been a signif-
icant part of the smart grid. It ensures sustainability and helps
utilities to take cost-efficient measures for power system planning
and operation. Conventional methods for load forecasting cannot
handle huge data that has a nonlinear relationship with load
power. Hence an integrated approach is needed that adopts a
coordinating procedure between different modules of electricity
load forecasting. We develop a novel electricity load forecasting
architecture that integrates three modules, namely data selection,
extraction, and classification into a single model. First, essential
features are selected with the help of random forest and re-
cursive feature elimination methods. This helps reduce feature
redundancy and hence computational overhead for the next two
modules. Second, dimensionality reduction is realized with the
help of a t-stochastic neighbourhood embedding algorithm for the
best feature extraction. Finally, the electricity load is forecasted
with the help of a deep neural network (DNN). To improve the
learning trend and computational efficiency, we employ a grid
search algorithm for tuning the critical parameters of the DNN.
Simulation results confirm that the proposed model achieves
higher accuracy when compared to the standard DNN.
Index Terms—Big data, Electricity load forecasting, Feature
engineering, Classification, Smart grid
I. INTRODUCTION
Electricity is an expensive commodity and its consumption
must be synchronized with the generation to avoid wastage.
Today’s technologies do not allow to store or queue extra
energy in an economical manner. Also, due to the limited
transmission capacity of the existing power network, it cannot
be transported to other regions and hence makes electric-
ity characteristics local and time-varying in multiple aspects
among different regions. Electricity load in its nature is one of
the volatile and unpredictable commodities, and it can rise to
tens and sometimes hundreds of times to its average value.
The under/overestimation of power generation/consumption
can pose severe challenges to the power system network.
In other words, accurate forecasting and reducing the mean
absolute percentage error (MAPE) only by 1%is so impressive
and meaningful to have the impact of 3–5%on the generation
side. The overall impact of this decrease in generation can
reduce the generation cost of about 0.1%to 0.3%[1]. For
this reason, various Artificial Intelligence (AI) and Machine
Learning (ML) forecasting models are proposed to achieve
better accuracy in the power market.
In a deregulated environment of the power industry, the
role of electricity demand forecasting has become increas-
ingly important in the smart grid. The primary purpose of
price/load prediction is to minimize power demand peaks
and to balance the supply-demand gap. Among numerous
forecasting methods, short term load forecasting (STLF) aims
to predict the load from several minutes up to hours and
weeks into the future. An accurate and stable STLF strategy
brings an unprecedented level of flexibility for its management
and creates a win-win situation for both its generation and
consumption side stakeholders. The electricity load is affected
by many factors such as generation capacity, fuel prices,
renewable generation, etc., and most of the factors vary within
short intervals. Accurate forecasting is essential, but due to
the more extensive data, it is challenging to increase accuracy.
Smart meters continuously monitor the associated factors such
environment, RES generation, temperature, etc., all in real-
time; however, the amount of data available for forecasting is
considerably large and hence difficult to handle, especially for
STLF [2].
Residential buildings account for 20 – 40%of total energy
demand, and hence making buildings energy efficient is es-
sential for sustainable development of electric power systems.
Apart from a major source of energy consumption, buildings
are also identified for a substantial amount of energy wastage.
Hence, the role of STLF is critical to minimize energy wastage
at the building level and mitigating uncertainties for the
reliability of the grid [3].
Since the early 1990s, AI techniques have been widely
explored for STLF, and one of the popular AI methods is
Neural Network (NN). The NNs prediction models provide
promising prediction results, and that is the reason that they
are extensively used in different applications. However, NNs
undergoes a number of weaknesses, which includes overfitting
issue, estimation of connection weight, model construction,
and consideration of extensive data for model training. Due
to these reasons, it is challenging to employ NNs for STLF
problems [4]. In 1995, turkey et al. [5] proposed an inno-
978-1-5386-1370-2/18/$31.00 ©2018 IEEE 1
Fig. 1: Proposed system model
vative AI technique they called support vector machine and
support vector regressor to address the shortcoming of NNs.
These methods employ empirical risk minimization principles
to improve the training process and find globally optimal
solutions in the search space. However, these methods are
computationally costly and hence make the algorithm difficult
to converge. Also, these methods are not suitable for large data
sets and difficult to perform when the values of the training
class are overlapping.
For STLF strategies, most of the work is based either
on selection or classification methods where Decision Tree
(DT) algorithms and Artificial Neural Networks (ANNs) have
gained much attention. Both methods have limited capabilities
such as DT faces overfitting problems, which means that
model performance is good in training but not in prediction.
Similarly, ANN models have limited generalization capabil-
ities, limited control over convergence/stability, and limited
capabilities to deal with the uncertainty. Furthermore, the
learning-based model does not take into account the big
data characteristics, and the performance evaluation criterion
is based only on price/load data, which is not large. With
the consideration of big data characteristics, the forecasting
accuracy needs to be further improved.
The remaining sections of this paper are organized as fol-
lows. The survey of the proposed load forecasting framework
is described in sectionII. Feature selection and feature extrac-
tion methodologies are described in section III and section IV,
respectively. In section V, the Enhanced Convolutional Neural
Network (ECNN) classifier is demonstrated. Section VI shows
the experimental results for verifying our proposed framework.
The paper is concluded in section VII finally.
II. SYSTEM FRAMEWORK
Inspired from [5], the Fig. 1 shows the framework of the
proposed system that is based on three modules, namely
feature selection, extraction, and classification. The first part of
Fig. 1 corresponds to the feature selection which starts with
the standardization of the raw data. Standardization is very
crucial because it later affects the overall performance of the
classifier. After applying min-max standardization, data is fed
into the feature selector, which is based on Random Forest
(RF) and Recursive Feature Elimination (RFE) algorithms.
Feature selector decides whether a feature needs to be reserved
or removed before fed into feature extractor. A feature is kept
only in the feature selector index if selected from both RF
and RFE algorithms. To remove redundant features, the t-
Stochastic Neighbourhood Embedding (t-SNE) algorithm is
applied in the second stage. Finally, extracted features are fed
into the CNN classifier for building the forecast model. Since
CNN performance is controlled by many hyperparameters, we
use the grid search algorithm (GSA) to assign optimal values
to the parameters for better efficiency. The following three
sections describe the details of these modules.
III. FEATURE SELECTION
We propose a combined method based on two algorithms
to control the feature selection process. In this way, more
accurate features are selected to improve the forecasting
mechanism. First of all, RF is applied, which is an ensemble
learning technique and has a higher computational capability.
As the name suggests, it consists of RF with hundreds of
decision trees trained with the bagging method. RF grows on
bootstrap data sets to divide the data into feature bagging and
out of bag (OOB) data to best separate the samples. The OOB
data is used to calculate feature importance in the data set.
RF guarantees that all trees are decorrelated and, therefore,
reduce variance and overfitting problems of the decision tree
method. During the training process, each feature impact on
Gini impurity is calculated. A feature has more importance if
it decreases the Gini impurity. The final significance of the
variable is determined with high cardinality. Fig. 2 shows that
combined importance scores add up to 100%, and clearly, 10
out of 15 features are the most prominent features contributing
(>0.80) to the creation of the model.
Fig. 2: RF grades of each feature (DA_CC, DA_MLC,
RT_CC, and OTHER have low grades obviously)
The second method employed for finding an optimal number
of features is RFE with Cross-Validation (RFECV). Contrary
to the RF method, RFECV recursively eliminates highly
correlated in the data set. Highly correlated features give the
same results and bring high computational complexity during
classification. With the help of the feature selection process,
much computational overhead is reduced to train the model.
Fig. 3 shows that the RFECV achieves (>0.85) score when
six informative features are found. The performance of the
curve gradually decreases when non-informative features are
added to the model. The shaded area in the curve shows the
variability of cross-validation above and below the mean score.
Initially, 15 features are fed, and their cumulative score jumps
low to high when 6–8 features are found and declined again
from the optimal number of features. Both feature selectors
work independently and can be deployed distributedly to
achieve computation efficiency. To select the best ten features,
we introduce a threshold (TRF 0.07) for RF. The RFECV
provides the list of ten best features. A combination of RF
and RFE selects the most important features. There exists a
redundancy among ten best-selected features for which they
are sent to the t-SNE algorithm for feature extraction.
IV. FEATURE EXTRACTION
Feature extraction is useful to remove redundant features,
and a model generalizes better when appropriate features are
used during the fitting process. To reduce the redundancy
among features, PCA, and classical multidimensional scaling
are the most common methods for feature extraction. However,
these techniques assume a linear mapping from high to low
dimension space [6]. Fig. 4 clearly shows that PCA makes
the clusters of non-linear data that are entirely overlapping
and results in high dimension mapping. Data in electricity
load forecasting needs to be non-linear mapped for appropriate
embedding into low dimension.
Fig. 3: Number of optimum features selected by RFE
To addressed non-linear data mapping issues, Kernel PCA
(KPCA) is used, which is an extension of PCA. However,
KPCA requires multiple hyperparameters of the kernel func-
tions to be tuned, which increases computation time and hin-
ders the performance. Moreover, KPCA is not as interpretable
as PCA because it is not possible to determine how much
variance is explained by individual dimensions [7].
To address the above-mentioned issues in PCA and KPCA,
we employ t-SNE [8] to perform non-linear mapping and
dimension reduction of data altogether. The t- SNE uses
"stochastic neighbours," which means not to have a clear
border to distinguish how multiple data points are neighbours
of the other locations. This is a significant advantage of t-SNE
to take both local and global structures into considerations.
Finding local and global structure simultaneously create a
well-balanced dimensionality reduction map. The aim is to
preserve the maximum possible useful high dimensional data
points into the low dimension map. Fig. 5 shows how the data
points from the different clusters are well separated in the two-
dimensional space. The ten best-selected features are used as
an input of t-SNE and the output matrix is expressed as,
X= (x1, x2, x3, ..., xN)T(1)
where xiis the ith feature of electricity load. In the t-
SNE algorithm, two essential steps are performed. First, in
high dimensional data space, a probability distribution Pis
constructed. Given a set of Nhigh dimensional objects, a
data point xiwould pick xjas its neighbour if its probability
is in proportionate to the probability density of a Gaussian
centred on xi. The conditional probability(pj|i)for picking
a nearby data point is relatively high, whereas, for faraway
data points, it is almost negligible. Mathematical expression
for construction Pdistribution is given by,
pj|i=exp−||xix2
j||/(2σ2
i)
Pk6=iexp−||xix2
j||/(2σ2
i)(2)
such that the probability of selecting the pair xiand xjis,
pij =pi|j+pj|i
2N(3)
Fig. 4: Performance of PCA on dimensionality reduction
Fig. 5: Performance of t-SNE on dimensionality reduction
The probabilities pij = 0 for i=j. In Eq. 2, σrepresents
the bandwidth of the Gaussian kernel to set the perplexity of
the conditional distribution. Perplexity indicates how well the
bandwidth of local and global aspect is adapted according to
the density of data. The perplexity value has a complex effect
on prediction and model fitting of a sample. To achieve a target
perplexity, the value of bandwidth σiis adjusted according to
the data density.
For the construct of d-dimensional map yi, ..., yNwhere
yiRd, second phase of t-SNE defines probability density
distribution,Q, through perfect replication of high dimensional
data points (xi,xj) into low dimensional data points (yi,yj).
Mathematically, qij is defined as following,
qij =(1 + ||yiyj||2)1
Pk=l(1 + ||ykyl||2)1(4)
The Student’s t-distribution is used to measure the similarities
of high dimensional data in qij . To obtain the yi, the Kullback
Leibler divergence between high and low dimensional space
is minimized as follows,
KL(P||Q) = X
i6=j
pij log pij
qij
(5)
In fact, this result reflects the similarities between the high-
dimensional inputs very well. After describing feature selec-
tion and feature extraction, we propose the ECNN classifier
in the next section to perform the final electricity load fore-
casting.
V. OPTIMAL CLASSIFICATION
Since CNN is robust and efficient enough in electricity
load data, we choose CNN as the classifier. In this section,
the classification problem is investigated first. After that, the
GSA based CNN is proposed to optimize this problem. The
main goal of this work is to minimize the cross-entropy loss
function of CNN. However, there is a strong link between
the loss function and value of CNN super parameters. It is
very challenging to obtain the optimal value of these super
parameters to achieve better efficiency and higher accuracy.
In this work, we employ GSA to tune these parameters.
In essence, CNNs are a special kind of neural network,
which processes data that has grid topology. In this perspec-
tive, images are formed because of 2D grids, and time-series
data such as electricity load and price data are viewed as a
1D grid. Among multiple layers, at least one layer of CNNs is
dedicated to performing convolutions for specific linear opera-
tion. The output of the convolution layer for multidimensional
input is calculated with the following equation,
S= (xw)(6)
where xis the input function, and wdenotes the weighting
function, also called the filter or kernel of a CNN. The
output is in the form of a feature map, denoted by S. The
inputs and weights of a CNN are multidimensional arrays.
During the course of iterations, random weights are assigned
to each input for training purposes. The convolution operation
for a two-dimensional input can be expressed as:
S(i, j)=(IK)(i, j) = X
lX
m
I(l, m)K(i+l, j +m)
(7)
where Iand Krepresent two-dimensional input and kernel,
Sis the resulting feature map after applying the convolution
operation. In reality, there are three phases to complete the
operation of the convolutional layer. As a first step, a feature
map is obtained after performing a convolution operation.
Then, a nonlinear activation function is applied to all the
elements of the feature map. The Rectified Linear Activation
function (ReLU) is the preferred function to faster the training
process.
Finally, to achieve a modified and desired feature map, a
pooling function is employed. The purpose of pooling opera-
tion is to reduce the dimensionality and amount of parameters,
thus making the network less susceptible to small variations
in the input. In this work, we use the max-pooling method
Fig. 6: Performance on load forecasting using CNN and
ECNN
to avoid overfitting and computational complexity. In max
pooling, the operation chooses the maximum value within a
matrix and discards the lower value to provide an abstracted
form of representation.
As stated, the designed framework can be formed with one
or more convolutional layers. In the end, the produced outputs
of the convolutional layer(s) are sent to one or more fully
connected layers to extract the features. In principle, fully
connected layers are the same as hidden layers in a traditional
multi-layer perceptron neural network. The output of fully
connected layers in the form of a flatten matrix is given to
the output layer for classification. The function of the output
layer is similar to the output layer in a standard ANN. The final
convolution involves backpropagation for the learning process
to weigh the end product accurately.
A. The Grid Search Algorithm
In the proposed framework, we employ GSA to choose
optimal values for the dropout rate (0.2–0.8), learning rate
(0.2–0.8), epochs, and the number of neurons in the standard
CNN. The main reason for choosing these parameters is that
little variations in the values can affect the performance of
CNN many folds. Among different optimization techniques,
GSA is seen as one of the fundamental tools to find the best
combinations of parameters as a search problem. GSA tries
all candidate solutions on a grid and chooses the best one
in terms of the fitness function. It is a simple and straight
forward method to reduce the computational overhead. The
optimization problem in GSA is defined as,
max F (θ1, θ2, ..., θn)
s.t θmin,i θiθmax,i,(i= 1,2, ..., n)(8)
where F()i denotes fitness function and θiis the i-th decision
variable. There are two main steps in a standard grid search
method, namely, grid creation and grid validation. First, a set
of grids parameters are generated as the candidate solutions in
the form of dictionary. These candidates solutions contain an
Fig. 7: Performance on load forecasting using CNN and
ECNN
Day Ahead Week Ahead
Forecast Horizon
0
5
10
15
20
25
MAPE
CNN
ECNN
Fig. 8: MAPE comparison between CNN and ECNN
equal interval [di=θmax,iθmin,i
mi]for the decision variable
i, where mirepresents sum total of all candidates. Similarly,
the j-th candidate solution for variable i,θi,j , is expressed as
follows,
θi,j =θmin,i (j= 1)
θmin,i +midi(j= 2,3, ..., mi)(9)
As a second step, all candidate solutions are tried on the
created grids to find the optimal solution θ
1, θ
2, ..., θ
n. It
reaches the best set of parameters from a set of values. In
this work, the fitness function is designed as follows,
F=1
3ac(T r) + 1
3ac(V s) + 1
3
1
|ac(T r)ac(V s)|(10)
where ac(T r)and ac(V s)represent the average prediction
accuracy for the training and validation datasets of the ECNN
model, respectively. According to Eq. 10, an optimal value of
hyperparameters needs to guarantee accurate prediction and
avoid the overfitting problem during learning. In the proposed
framework, the GSA searches the optimal values of the defined
hyper parameters in an array.
VI. SIMULATION SETUP AND RESULTS
In this section, we evaluate the performance of the proposed
framework. The python simulator is developed according to
the system framework devised in section II. For this frame-
work, input data contains energy generation data and hourly
electricity load data of the ISO New England Control Area
(ISO NE-CA) from 2010 to 2015 [9]. This record consists of
over 50000 real-world electricity price records. The simulation
results are organized as follows:
1) Performance of Hybrid Feature Selection:Important
features in ISO NE-CA are roughly selected from hourly
electricity load data from 1-1-2015 to 31-12-2017. During
the feature selection process, every feature sequence takes the
form as a vector. The feature value in different timestamps is
represented as components of this sequence. Since our goal is
to predict the electricity load, which is named "System load"
in the data and those features that have little effect on the
load are removed. First of all, RF is applied to calculate the
feature importance, as shown in Fig. 3. The optimum number
of features graded by RFE method is shown in Fig. 4, which
indicates that 6–8 most important features achieve above 84%
score. We drop five features with obvious low grade, i.e.,
features DA_CC, RT_MLC, RT_CC, DA_MLC, and RSP. It is
pertinent to mention here that with the increase in the threshold
value, more features are dropped, resulting in the increase of
training speed and the decrease of accuracy.
2) The t-SNE Performance Comparision with PCA:
In order to eliminate the redundant information within the
features, two principal components PC1 and PC2 are extracted
with t-SNE and PCA. PCA is a linear algorithm, and it does
not interpret the complex polynomial relationship between
features, while t-SNE captures the exact relationship between
data points. PCA performs a linear mapping of the data to a
lower-dimensional space in such a way that the variance of
the data in the low-dimensional representation is maximised.
As shown in Fig. 4, PCA concentrates on placing dissimilar
data points far apart in a lower dimension representation
with higher ranges. The t-SNE extracts most of the principal
components, as shown in Fig. 5 within a low range. Thus,
we select the t-SNE to guarantee the accuracy of forecasting.
The data points of t-SNE distribute along coordinate axes, i.e.,
extract the principal components that are more representative
than the PCA.
3) ECCN Performance Comparision with Standard CNN:
We compare the performance of ECNN with benchmark CNN
classifier to forecast day-ahead electricity load. To comprehen-
sively understand the characteristic of the proposed method,
we calculate the MAPE as a performance indicator. This is
expressed as the following,
M AP E =1
N
N
X
i=1
|(yiˆyi)2|
yi
×100% (11)
In Eq.11, yiand ˆyiare the actual and forecasting values,
respectively. As shown in Figs. 6 and 7, the ECNN is demon-
strated as an improved model both for the day-ahead and
week-ahead load forecasting strategies. Fig. 8 clearly shows
that the MAPE values of CNN are much higher for both day-
ahead and week-ahead forecasting as compared to the ECNN
values in the same scenarios. The GSA helps optimise the
super parameters of CNN jointly; therefore, ECNN performs
better in terms of the accuracy of electricity load forecasting
than the CNN.
VII. CONCLUSION
In this work, we investigated the short term electricity load
forecasting problem, while considering feature engineering
and classifier parameters adjustment. We proposed a two-
stage electricity load forecasting framework, which is based
on feature processing and enhanced CNN classifiers to
solve forecasting accuracy problems. Specifically, to select
the critical features, a new combined two-stage model is
employed to process the n-dimensional time sequence data
as input. Additionally, to enhance CNN classifier efficiency
in terms of accuracy and speed, we apply t-SNE for
feature extraction with less redundancy. Moreover, the GSA
automatically and efficiently obtains the appropriate super
parameters for ECNN to boost classification performance.
The numerical results confirm that the proposed framework
shows better results in terms of accuracy when compared
to the standard CNN. Furthermore, the work suggests GSA
offers a flexible and powerful tool for certain types of
optimzation problem. In a different context, for example,
GSA has a strong potential to be used for research into robot
control systems for nuclear decommissioning and mobile robot
path planning, which the present authors are also investigating.
Acknowledgements The authors acknowledge funding
support from COMSATS University Islamabad and Lancaster
University UK to support this project. The work was in part
supported by the UK EPSRC grant EP/R02572X/1.
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In the past decade, the world's energy consumption is increasing largely, while residential buildings are the primary sector consuming about a quarter of the total energy produced. The researchers have made significant efforts to reduce energy usage in previous years by implementing energy monitoring and prediction techniques. Further, these techniques have been utilized for energy optimization in residential buildings and provide the consumer awareness about the usage patterns. In this paper, intelligent energy aware approaches have been reviewed by focusing on energy monitoring, prediction, optimization and performance evaluation using benchmark energy datasets. This review has been concluded with a discussion on future research directions for improving energy aware approaches in residential buildings.
... consumption in residential buildings. Random forest algorithm is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model [43,53] and more accurate predictions were achieved. Feature extraction is essential for handling the redundant feature, so principle component analysis(PCA) has been deployed [53] and produced the patterns of appliance usage at a specific time. ...
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In the past decade, the world’s energy consumption is increasing largely, while residential buildings are the primary sector consuming about a quarter of the total energy produced. The researchers have made significant efforts to reduce energy usage in previous years by implementing energy monitoring and prediction techniques. Further, these techniques have been utilized for energy optimization in residential buildings and provide the consumer awareness about the usage patterns. In this paper, intelligent energy aware approaches have been reviewed by focusing on energy monitoring, prediction, optimization and performance evaluation using benchmark energy datasets. This review has been concluded with a discussion on future research directions for improving energy aware approaches in residential buildings.
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Short term load forecasting methods: A review
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  • Devender Singh
AK Srivastava, Ajay Shekhar Pandey, and Devender Singh. "Short term load forecasting methods: A review ".In: Emerging Trends in Electrical Electronics & Sustainable Energy Systems (ICETEESES), International Conference on. IEEE. 2016, pp. 130138.
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