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Big Data Analytics for Load Forecasting in Smart Grids: A Survey

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Abstract and Figures

Recently big data analytics are gaining popularity in the energy management systems (EMS). The EMS are responsible for controlling, optimization and managing the energy market operations. Energy consumption forecasting plays a key role in EMS and helps in generation planning, management and energy conversation. A large amount of data is being collected by the smart meters on daily basis. Big data analytics can help in achieving insights for smart energy management. Several prediction methods are proposed for energy consumption forecasting. This study explores the state-of-the-art forecasting methods. The studied forecasting methods are classified into two major categories: (i) univariate (time series) forecasting models and (ii) multivariate forecasting models. The strengths and limitations of studied methods are discussed. Comparative anlysis of these methods is also done in this survey. Furthermore, the forecasting techniques are reviewed from the aspects of big data and conventional data. Based on this survey, the gaps in the existing research are identified and future directions are described.
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Big Data Analytics for Load Forecasting in Smart
Grids: A Survey
Sana Mujeeb1, Nadeem Javaid1,, Sakeena Javaid1, Asma Rafique2, Manzoor Ilahi1
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
{sana.mujeeb.22, nadeemjavaidqau, sakeenajavaid}@gmail.com, tamimy@comsats.edu.pk
2Department of Computer Engineering, School of Sciences, Karabuk University, Turkey
asmamcs@gmail.com
Correspondence: nadeemjavaidqau@gmail.com; www.njavaid.com
Abstract—Recently big data analytics are gaining popularity in
the energy management systems (EMS). The EMS are responsible
for controlling, optimization and managing the energy market
operations. Energy consumption forecasting plays a key role
in EMS and helps in generation planning, management and
energy conversation. A large amount of data is being collected
by the smart meters on daily basis. Big data analytics can
help in achieving insights for smart energy management. Sev-
eral prediction methods are proposed for energy consumption
forecasting. This study explores the state-of-the-art forecasting
methods. The studied forecasting methods are classified into
two major categories: (i) univariate (time series) forecasting
models and (ii) multivariate forecasting models. The strengths
and limitations of studied methods are discussed. Comparative
anlysis of these methods is also done in this survey. Furthermore,
the forecasting techniques are reviewed from the aspects of big
data and conventional data. Based on this survey, the gaps in
the existing research are identified and future directions are
described.
Index Terms—Big Data, Data Analytics, Load Forecasting,
Artificial intelligent Forecasters, Deep Learning.
I. INT ROD UC TI ON
The modernization of power systems has brought a revo-
lution in the electricity generation and distribution sectors in
recent years. With the introduction of smart grid, the commu-
nication technology is integrated with conventional electricity
meters, known as smart meters. These smart meters measure
electricity consumption (and other measurements) at every
small time intervals and communicate to energy suppliers,
resulting in generation of very huge amount of data. Due
to availability of the huge amount of data, many innovative
programs are implemented like real-time pricing, off peak time
usage lesser tariffs, etc. In near future, all the conventional
energy meters will be replaced by smart meters. It is estimated
that, more than 800 million smart meters will be deployed
world wide till 2020. Power utilities receive a deluge of data
after the deployment of smart meters. This data is termed as
energy big data. Big data have a few major characteristics
referred as 4 V’s.
Volume: The major characteristic that makes data big is
its huge volume. Tera bytes and exabytes of smart meter
measurements are recorded daily.
Velocity: The frequency of recorded data is very high.
Smart meter measurements are recorded in very small
time intervals. It is a continuous streaming process.
Variety: The data can be in different structures, e.g., sen-
sors data, smart meters data and communication modules
data are different. Both structured and unstructured data
is captured. Unstructured data is standardized to make it
meaningful and useful.
Veracity: The trustworthiness and authenticity of data is
referred as veracity. The recorded data may have noisy
or false readings. The false readings can be due to the
malfunctioning of sensors.
TABLE I: List of abbreviations
Abbreviation Full Form
ABC Artificial Bee Colony
AEMO Australia Electricity Market Operators
ANN Artificial Neural Networks
ARIMA Auto Regressive Integrated Moving Average
CNN Convolution Neural Networks
CART Classification and Regression Tree
DNN Deep Neural Networks
DSM Demand Side Management
DT Decision Tree
DE Differential Evaluation
GA Genetic Algorithm
ISO NECA Independent System Operator New England
Control Area
KNN K Nearest Neighbor
LSSVM Least Square Support Vector Machine
LSTM Long Short Term Memory
MAPE Mean Absolute Percentage Error
NYISO New York Independent System Operator
PJM Pennsylvania-New Jersey-Maryland (Inter-
connection)
RMSE Root Mean Square Error
RNN Recurrent Neural Network
SAE Stacked Auto Encoders
STLF Short Term Load Forecast
SVM Support Vector Machine
International Conference on Cyber Security and Computer Science (ICONCS’18),
Oct 18-20, 2018 Safranbolu, Turkey
193
Fig. 1: Classification of prediction models.
TABLE II: List of symbols
Symbol Description
bSVM bias
ccost penalty
ηinsensitive loss function parameter
ρSVM marginal plane
σSVM kernel function parameter
wiANN weights
Whx RNN weights
Besides, the 4V’s of big data, the energy big data exhibits
a few more characteristics: (i) data as an energy: big data
analytics should cause energy saving, (ii) data as an exchange:
energy big data should be exchanged and integrated with other
sources big data to identify its value, (iii) data as an empathy:
data analytics can help in improvement of service quality of
energy utilities [1]. Approximately 220 million smart meter
measurements are recorded daily, in a large sized smart grid.
In order to avoid the failure of electricity distribution net-
works, the suppliers rely on generation and demand balancing.
For balancing demand generation and filling the demand
response gap, the utilities have to estimate the energy demand
patterns of different consumers. The demand pattern is not
always even, therefore electricity load estimation is a very
difficult task. Several prediction methods are proposed for
energy load forecasting. Classic statistical methods to modern
computationally intelligent prediction techniques are proposed
for electricity load prediction. This work surveys the state-
of-the-art load forecasting models from the literature of past
four years. The focus of this survey is on the univariate and
multivariate prediction models. The major contribution of this
work is the comparative analysis of prediction methods with
respect to their input, i.e., conventional traditional data and
big data. Energy big data is also explained. The existing load
forecasting surveys mostly focus on traditional data forecasting
techniques [2]-[5]. The existing surveys and reviews, discuss
only one or two forecasting horizons (short-term, medium-
term). Whereas, all the forecasting horizons, i.e., short-term,
medium-term and long-term are discussed in this study. An
analysis is presented on electricity load forecasting with big
data approaches [6]-[16] and conventional data [17]-[36].
List of abbreviations used in this article is given in Table
1 and list of symbols is shown in Table 2. A comparison
of traditional and big data analysis is presented in Table 3.
Rest of the paper is organized as: Section 2 is comparison of
forecasting models, Section 3 is critical analysis and section
4 is conclusion.
II. CO MPAR IS ON O F FOR EC AS TI NG MO DE LS
In this section, the forecasting models are categorized as:
uni-variate (time series) models and multi-variate models.
Brief explanation of sub categories of these models, is also
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TABLE III: Comparison of traditional data and big data analysis.
Feature Traditional Data Big Data
Size Limited size Very huge (terabytes, exabytes)
Sources Power utilities production data only All the influential factors, e.g., population, weather, economic
conditions, government policies, customer behavior patterns,
etc.
Algorithms Classical, statistical, Machine learning, AI Feature extraction, correlation analysis, dimension reduction,
deep learning, parallel processing algorithms
Accuracy High for short term predictions, degrades with noisy data Accurately model noisy and data, risk of falling in local
optimum
Usage / benefits Can impact decisions in the present, i.e., short-term decision
making, used for analysing current situations and short-
term forecasting, online monitoring, fault detection (instant
response to the situation)
Helps in: long-term decision making, budgeting, investment,
policy making, assets allocation, maintenance planning, re-
cruitment strategies etc.
given. The classification hierarchy of prediction models is
shown in Fig. 1. Moreover, a comparative analysis of the
discussed models is given at the end of this section.
4.1 Load Forecasting based on Time series Models
Electricity consumption recorded at successive equally
spaced time intervals is known as electricity consumption time
series. Time series forecasters predict the future values based
on previously observed values. Following are few popular time
series prediction models implemented for forecasting energy
consumption.
4.1.1 Autoregressive Integrated Moving Average
ARIMA is the most popular method for time series fore-
casting. First introduced by Jinkens et al., ARIMA [37] is also
known as Jenkins-Box approach. It can calculate the proba-
bility of a future value lying in a specified range of values.
ARIMA is combination of Auto-Regression (AR) and Moving
Average (ML). AR process means that the current value of
the series depends on the previous values of same series.
ML is a process which assumes that the current deviation
of a value from the mean of series depends on the previous
deviation. ARIMA is donated as ARIM A(p, q , d), where pis
the number of autoregressive terms, qis the number of non-
seasonal differences and qis the number of lagged forecast
errors (from the prediction equation). Three basic steps of
ARIMA are: model identification, parameter estimation and
model verification (shown in Fig. 2). For establishing the
forecasting equation of ARIMA, the base are the following
equations [34]:
F or d =0:yt=Yt(1)
F or d =1:yt=YtYt1(2)
F or d =2:yt= (YtYt1)(Yt1Yt2)(3)
Where yis the dth difference of Y. From the above equations,
the generalized equation of ARIMA forecaster can be written
as follows:
ˆyt=ε+φ1yt1+... +φpytpθ1et1... θqetq(4)
Where, εis error term, φis the parameter of the auto regressive
part and θis the moving average parameter.
Fig. 2: Steps of ARIMA prediction model.
4.1.2 Artificial Neural Network
ANN is network of interconnected small computational
units called neurons, inspired by biological neurons. Equation
of multi layer perceptron (shown in Fig. 3) neural networks is
given below:
y(x1, . . . , xn) = f(w0+w1x1+. . . +wnxn)(5)
Where, xiare the inputs, f() is input to output mapping
function, wiare the weights and w0is the bias. The function
is given by following equation:
f(v) = 1
1 + ev(6)
The output activation function can be written as following that
is a simple binary discrimination (zero-centered) sigmoid:
f(v) = 1ev
1 + ev(7)
ANN models can be used for prediction of both time series
and multivariate inputs. Some of the popular time series ANN
prediction models are Elman network [12], ELM [22],[34],
NARX and LSTM [17].
4.1.2.1 Non-linear Autoregressive Network with Exogenous
Variable
NARX ia a non-linear and autoregressive recurrent neural
network (RNN). It has a feedback architecture, in which output
layer is connected to the hidden layers of the network. It is
different from back propagation ANN (shown in Fig. 3), as
its feed back connection encloses several hidden layers, and
not the input layer. NARX also utilizes the memory ability
by using the past predicted values or actual observations. It
models a nonlinear function by recurrence from the past values
of the time series. This recurrence relation is used to predict
the new values in time series. The input to the network is
the past lagged values of the same time series. For example,
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x1
xn
x2 ås
.
.
.
Input Layer Hidden Layer Output Layer
.
.
.
w11
w1n
w12
w21
w22
w2n
Y
Output
I
n
p
u
t
s
Back propagation of weights
Fig. 3: Simplest ANN: multilayer perceptorn with back propagation of weights.
to predict a future value yt, the inputs of the network are
(yt1, yt2, ..., ytp). While the training of network the past
predicted values are also used as an input. NARX can be
defined by the following equation:
ˆyt+1 =F(yt, yt1... ytn, xt+1, xt, ..., xtn) + εt(8)
Where, ˆyt+1 is output of network at time t, that is the one
step ahead predicted value of future time, t+ 1. F(.) is the
non-linear mapping function of the network (e.g., polynomial,
sigmoid, etc.), yt, yt1, ... are the true past observations also
called the desired outputs, xt+1, xt, ... are the network inputs
that are the lagged values of the time series, nis the number
of delays and εtis the error term. NARX network is shown
in Fig. 4.
4.1.2.2 Long Short Term Memory
LSTM is a deep learning method that is variant of RNN.
It is first introduced by Hochreiter et al. in 1997 [38]. The
basic purpose of proposing LSTM was to avoid the problem
of vanishing gradient (using gradient descent algorithm), that
occurs while training of back propagation neural network
(BPNN) (shown in Fig. 3).. In LSTM every neuron of hidden
layer is a memory cell, that contains a self-connected recurrent
edge. This edge has a weight of 1, which makes the gradient
pass across may steps without exploding or vanishing [17].
4.1.3 Comparative Analysis of Time Series Forecasting Models
ARIMA is better suited to short-term forecasting, on the
other hand, ANN models perform better at long-term fore-
casting. ANNs can detect the underlying patterns of the data
with the help of hidden layer nodes, therefore, they can model
non-stationary time series [12],[22],[34]. A major benefit of
neural network is their ability to flexibly create a nonlinear
mapping between input and output data. They can capture the
nonlinearity of the time series very well.
4.2 Load Forecasting based on Multivariate Models
Multivariate models take multiple inputs. These inputs are
the factors that influence the electricity consumption, also
called exogenous variables. These variables can be weather
parameters (temperature, humidity, cloud cover, wind speed,
etc.), calendar variables (hour of the day, day of the week,
etc.), fuel price etc. Multivariate forecasting methods are
categorized into three main categories, i.e., ensemble, hybrid
and deep learning models. Brief description of these categories
and the papers implemented these methods for electricity load
forecasting, is given in this section.
4.2.1 Load Forecasting based on Ensemble Models
Ensemble methods are the prediction models that combine
different learners in order to achieve better performance.
Ensemble models are supervised learning techniques. Multiple
weak learning methods are combined to establish a strong and
accurate model. Ensemble method is a combination of multiple
models, that helps to improve the generalization errors which
might not be handled by a single modeling approach (shown
in Fig. 5).
Let us assume, there are three prediction models: A, B and C
and their prediction accuracy is 88%, 83%, 76% respectively.
Suppose, A and C are highly correlated and model B is not
correlated with both A and C. In such a scenario, combining
models A and C will not reduce the prediction error, however
combining model B with model A or model C would improve
the accuracy. Every prediction method is assigned a certain
weight. These weight are assigned by the standard techniques.
Following are some weight assigning techniques:
Collinearity calculation: Calculate the collinearity of all
models which each other in order to decide the base
models. Exclude the highly correlated models so that
the final model is generalized enough to generate less
generalization error.
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Weight assignment by ANN: Neural Networks can be used
to determine the appropriate weights for the prediction
models.
Weight assignment by Bayesian: Weights are assigned by
calculating the posterior probability of all the models.
One of the two techniques can be used: (i) Bayesian
model averaging that is an in-sample technique, (ii) Pre-
dictive likelihood scaling that is an out-of-bag technique.
Equal weight assignment: Assign equal weights to all the
models. This is the simplest method and often performs
well as compared to the complex methods. However, it
is unable to rank the models based on their performance.
Other approaches include bagging, boosting of input
samples, learner’s forward selection, etc.
4.2.1.1 Random Forest
Random forest (RF) is one of the most popular ensemble
learning model. From a large sized data, samples are drawn
with replacement that are subsets of data’s features. Random
samples are taken from the data to establish decision trees
(DT). Several DT are made with these randomly drawn data
samples, that makes a random forest. DT can be made using
any tree generation algorithm, e.g., ID3, CART (Classification
And Regression Tree) or c4.5, etc. The parameters of RF algo-
rithm are number of trees and decision tree related parameters
like split criteria. For example, 100 trees are generated from a
data. A test sample is given for prediction, every tree generates
a response to the test sample, that makes 100 predictions for
a test sample. A weighted average of these responses is the
final predicted value of the random forest. There are many
trees in the forest made with different data samples, therefore,
the prediction model is highly generalized with no possibility
of overfitting.
In paper [24], authors have predicted short-term electricity
load of a university campus building using random forest.
A two staged models is proposed for load prediction. In the
first stage, the electricity consumption patterns are considered
using the moving average method. In the second stage, RF
is trained with the optimal hyper parameter, i.e., number of
trees, split criteria of decision tree, minimum split, etc. The
optimal parameters are selected by trial and error method. The
model is trained on five years hourly load data. The trained
model is verified by modified Time Series Cross Validation
(TSCV). The performance of a prediction method degrades if
the difference between the training time and prediction time,
is very large. This problem arises when training data is much
larger as compared to the test data. To overcome this problem,
TSCV is applied for one step ahead forecast (point forecast).
This proposed model outperforms SVR and ANN in terms of
MAPE and RMSE. Results prove the effectiveness of proposed
method for short-term load forecasting.
4.2.2 Load Forecasting based on Hybrid Models
Hybrid forecasting methods are combination of data
smoothing, regression and other techniques. Hybrid ap-
proaches combine the strengths of two or more methods while
mitigating their individual weaknesses. Generally, a meta
heuristic optimization algorithm is combined with forecasting
method, to fine tune the hyper parameters of the forecaster.
To train an accurate model on the training data, the hyper
parameters of model must be chosen according to the data.
Default hyper parameters do not guarantee good training for
every input data.
4.2.2.1 Hybrid Support Vector Machine
SVM is a really efficient prediction method. Due to its
computational simplicity and accuracy, it is one of the most
used methods for prediction. SVM was originally proposed by
Vapnik et al. in 1995 [39]. SVM create an optimal hyper plane
(exactly in the middle) to divide training examples into their
respective classes. SVM has three main hyper parameters that
are: cost penalty c, insensitive loss function parameter ηand
sigma kernel parameter σ. SVM predictor can be written in
the form of following equation:
g(x) = signX
i
yiαiK(xi, x) + b(9)
=signX
i:yi=1
αiK(xi, x)X
j:yj=1
αjK(xj, x) + b
(10)
=signh+(x)h(x) + b.(11)
For a two class problem, the following discriminant can be
used:
s(x) = sign[p(x|1) p(x| − 1)],(12)
by assuming equal class priors p(1) = p(1). Suppose, the
class conditional densities use Parzen estimates:
p(x|1) p(x| − 1) = PiβiyiK(x, xi)
2Piβi
,(13)
Where,
βi0,(14)
X
i
βiyi= 0,(15)
Essentially we are picking weights or a distribution of the
examples while remaining consistent with the equal class
priors assumption.
Now the margin of an example under this discriminant is
mi=yis(xi) = yi[p(xi|1) p(xi| − 1)],(16)
that is a measure of correctness of the classified examples.
In other words, large and positive margins correspond to
confident and correct classifications.
In [33], the authors optimize the hyper parameters of least
square SVM (LSSVM), by using modified ABC optimization
algorithm. The hybrid model outperform several prediction
models. In [35], the author utilize hybrid SVR for prediction of
electricity load. The hyper parameters of SVR are tuned using
modified firefly optimization algorithm. Firefly algorithm (FA)
197
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xn
x2
D
D
D å s
.
.
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Input Layer Hidden
Layer 1 Output Layer
.
.
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w11
w1n
w12
w21
w22
w2n
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Output
I
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p
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Hidden
Layer 2
Time
Delay
Layer
w31
w32
w3n
Fig. 4: NARX network.
Fig. 5: General representation of ensemble models.
is a nature inspired meta heuristic optimization approach, that
is based on flashing behavior of fireflies. The original FA has
a possibility of trapping into local optimum. To overcome this
issue, two modifications were suggested by the authors. Firstly,
improving the population diversity by the aid of two mutations
and three cross over operations. Secondly, encouraging the
total firefly population to move toward the best promising
local or global individual. The SVR model is optimized using
enhanced FA. The prediction results proves the effectiveness of
this hybrid model. It outperforms several prediction methods,
i.e., ANN, ARMA, PSO-SVR, GA-SVR, FA-SVR, etc.
4.2.2.2 Hybrid ANN
The performance of ANN depends on how well the model
is fit on the training data. The hyper parameters of ANN
are number of neurons, number of hidden layers, learning
rate, momentum and bias. A hybrid ANN prediction model is
proposed in [18]. The hyper parameters of ANN are optimized
using genetic algorithm. The results prove the efficiency and
good accuracy of proposed model as compared to other
models.
4.2.3 Load Forecasting based on DNN Models
DNN are variants of ANN, that has deep structure with
number of hidden layers cascaded into the network. Automatic
feature learning capability of DNN allows the network to learn
the non-linear complex function, and create mapping from
input to output without requirement of hand crafted features
[13],[17].
4.2.3.1 Stacked Autoencoder
Autoencoder is a feed forward neural network, that is a
unsupervised learning method. As the name suggests, au-
toencoders encodes the inputs by using an encoder function
y=f(x). The encoded values are reconstructed on the output
layer by passing through a decoder function x0=g(x). The
reconstructed out can be written as, x0=g(f(x)). Basically,
the inputs are copied to output layer by passing through hidden
layers. The purpose of using autoencoders is the dimension-
ality reduction of input data. In stacked autoencoder, multiple
encoding layers are stacked together as hidden layers of the
network as shown in the Fig. 6. The equation of autoencoders
is:
x0=g(wx +b)(17)
Where, x0are the reconstructed inputs, g(.)is the encoding
function, ware the weights and bis the bias.
4.2.3.2 Restricted Boltzman Machine
Visible units are conditionally independent on hidden units
and vice versa. For a RBM, energy function can be calculated
using following equation:
Energy(v , h) = b0hc0vh0Wv .
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Where b, c are offsets or biases and Wcomprises the weights
connecting units The joint probability of (v,h)
P(v, h) = 1
ZeEnerg y(v,h)
Where Zis the normalization term.
Given initial v(0), we sample h(0) sigm(W v (0) +c)
Then it can be sampled v(1) sigm(W0h(0) +b)
After tsteps, its obtain (h(t), v(t))
As t→ ∞, sample (h(t), v(t))are guaranteed to be
accurate sample of P(v, h)
4.2.3.4 Convolution Neural Network
CNN is a feed forward ANN, that perform mathematical
operation convolution on input data. Generally, CNN has three
basic layers that are used to build the network. These layers
are convolution, rectified linear unit (ReLU) and pooling layer.
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TABLE IV: Comparison of existing methods for load prediction.
Inputs Platform Duration Forecast Horizon Region Prediction Method Features Limitations
Historic energy consumption, de-
mand
Daily, hourly and 15 minutes en-
ergy consumption of entertainment
venues
2012-2014 Medium term, month
ahead
Ontario, Canada Artificial Neural Networks, SVR
[6]
Suitable for big data processing High time complexity
Historic load, weather data Hourly load of 1.2 million con-
sumers (residential, commercial,
industrial and municipal) of real
distribution system
2012 Short term, day and
week ahead
Not mentioned Hierarchical clustering (Bottom
up), Classification and regression
tree (CART) [7]
Computationally simple Unableto capture high nonlinearity
Temperature, humidity Global Energy Forecasting Compe-
tition 2012, hourly load and tem-
perature
2004-2007 Short term, Day and
week ahead
21 zones of USA Recency effect [8] Good performance on big data High complexity
Historical traffic, weather data Hourly traffic and weather data ob-
served on a national route from
Goyang to Paju, total 20.12 million
EVs
2014-2015 Short term, day
ahead
Traffic Monitoring
System (TMS) of the
Ministry of Land,
Infrastructure and
Transport (MOLIT),
South Korea
Decision Tree [9] Simple Unable to capture high nonlinearity
Historic load Every second load of three houses
of Smart dataset
May-July 2012 Short term, day and
week ahead
Umass Trace online
Repository
Adaptive Neuro Fuzzy Inference
System (ANFIS) [10]
Good accuracy, simple Hard to choose suitable kernel
method
Historic consumption 15 minutes consumption of Bud-
weiser Gardens event venue, total
43,680 measurements
January-March 2014 Short term, day and
week ahead
Ontario, Canada SVR [11] Simple and fast Accuracy degrades with extremely
nonlinear data
Historic consumption, weather pa-
rameters, social and economical
variables of smart city
North-eastern China smart city
dataset
2006-2015 Short-term, medium-
term
China Modified Elman Network [12] Efficiently capture nonlinearity,
good accuracy, high convergence
rate
High computational and space
complexity
Historic load 1.4 million hourly electricity load
records
2012-2014 Short term, day and
week ahead
Not mentioned K means, CNN [13] High accuracy High complexity
Historic load, electricity parame-
ters
Individual household electric
power consumption dataset
2006-2010 Short-term Not mentioned CNN [14] High accuracy, models big data
well
High complexity
Historic appliance consumption (i) Domestic Appliance Level Elec-
tricity dataset, (ii)Time series data
of power consumption, (iii) Syn-
thetic dataset
2012-2015 Short term (i) UK-Dale, (ii)
Southern England,
(iii) Canada
Bayesian network [15] Efficiently learns data patterns and
relationships in data, mitigate miss-
ing data, avoid overfitting
High complexity
Weather variables Historic temperature, humidity and
load data
2014-2016 Short-term Not mentioned MLR [16] Simple and fast Unable to deal with highly non-
stationary data
System load, day ahead demand,
weather data, hourly consumption
Hourly weather, consumption data
of New England
2003–2016 Short-term, day and
week ahead
ISO NE CA, New
England, USA
Empirical mode decomposition,
LSTM [17]
High accuracy, ability of accurately
predict long-term load
High complexity
Historic load Half hourly consumption data of
three states
2006-2009 Short-term New South Wales,
State of Victoria,
Queensland,
Australia
BPNN, RBFNN, GRNN, genetic
algorithm optimized back propa-
gation neural network (GABPNN),
cuckoo search algorithm [18]
Higher accuracy, outperforms com-
pared optimized ANN models
Possibility of stuck in local opti-
mum
Historic load 5 min ahead forecasting, Australian
electricity load data
2006-2007 Short term, hour
ahead
Australia MI, ReliefF, ANN, LR [19] Trained model on highlycorrelated
inputs, high accuracy
High complexity
Calendar variables, weather vari-
ables, lagged loads
15 minute electricity load of
"Smart Metering Customer Behav-
ior Trial" from 5000 homes of
Irish Social Science Data Archive
(ISSDA)
2009-2010 Ireland, New York Very Short-term, 15
minutes and hour
ahead
ANN [20] Robustness to noisy data, auto-
matic feature engineering
Computationally expensive,
requires large training data
Historical load 15 minutes load of individual
household meter data
2010-2012 Short-term Taipei, Taiwan Decision tree, BPNN [21] Robustness to noisy data, high ac-
curacy
High complexity, vanishing gradi-
ent problem leading to overfitting
Temperature, date type 30 minutes load from Smart meter
data of Irish households from the
Irish Social Science Data Archive
(ISSD), 3000 households
2009-2010 Short term Ireland K-mean, Online Sequential ELM
[22]
Fast in learning Difficult to select appropriate ker-
nel function
Temperature, annual holidays,
maximum daily electrical loads
EUNITE, a historical electricity
load dataset
1997-1998 Short term Middle region of the
Delta in Egypt
Hybrid KN3B predictors, KNN and
NB classifier [23]
High accuracy Computationally expensive
Historic load, weather variables Hourly load, temperature, humidity 2013-2015 Short term, day and
week ahead
Not mentioned Multi-variable linear regression
(MLR) [24]
Simple Unable to model highly nonlinear
data well
Historical temperature and power
load data
Hartcourt North Building of Na-
tional Penghu University of Sci-
ence and Technology
January-May 2015,
September-October
2015
Short-term Taiwan Multipoint fuzzy prediction
(MPFP) [25]
High accuracy High complexity
Historic load Real-time hourly load data (in
MWHrs.) of NSW State
April-October 2011 Short term, day and
week ahead
Australia RBFNN [26] High accuracy High complexity
Outdoor temperature, relative hu-
midity, supply and return chilled
water temperature, flow rate of the
chilled water
One-year building operational data
from campus building in the Hong
Kong Polytechnic University
2015 Short-term Hong Kong Decision tree model, association
rule mining [27]
Simple Accuracy degrades on noisy, miss-
ing data
Historic load EMS’s electricity information col-
lection system data
Not mentioned Short and medium-
term
Not mentioned Coordination optimization model
[28]
High accuracy High complexity
Weather data, electricity consump-
tion
15-minute intervals consumption
data of 5000 households from
project with Electric Power Board
(EPB) of Chattanooga
2011-2013 Short term, day and
week ahead
Chattanooga,
Tennessee, U.S.
Sparse coding, ridge regression
[29]
High accuracy High complexity
Historic price, meteorological at-
tributes
Hourly consumption of HVAC
system of a five-star hotel in
Hangzhou City
Not mentioned Short term, day
ahead
State Grid
Corporation of
China Hangzhou,
China
SVR [30] Simple Difficult to select appropriate ker-
nel function
Historic load data 10 minutes load of Belsito
Prisciano feeder Azienda
Comunale Energia e Ambiente
(ACEA) power grid, 10,490 km of
Rome city
2009-2011 Short term, 10 min-
utes and day ahead
Rome, Italy Echo State Network [31] High accuracy Trained network is a black-box,
cannot be understood
Indoor and outdoor temperature,
humidity, solar radiation, calendar
attributes, consumption
Consumption and weather of a uni-
versity of Girona’s office building
2013-2014 Short term, day and
week ahead
Not mentioned ANN, SVR, MLR [32] Regression models simpler and
faster than ANN, however less ac-
curate
ANN: high complexity, LR:unable
to capture high nonlinearity in data
Historical load and price Hourly price and load of NYISO,
PJM and New South Wales
2010, 2014 Short term, day
ahead
NYISO, PJM, NSW
AEMO energy mar-
kets
QOABC-LSSVM [33] High accuracy High complexity, possibility of
overfitting
Historic load Load Diagrams Dataset 2011-2014 Short-term Portugal ELM [34] High accuracy High complexity
Historic load Hourly consumption of 5 cities 2007-2010 Shoer-term FARS electric power
company
Firefly-SVR [35] High accuracy High complexity
200
In the convolution layer a convolution filter is applied
to extract features from input data [13]. The convolution
operation can be defined by following equation:
y(t)=(xw)(t) = Zx(a)w(ta)da (18)
Where, xis the input, wis the kernel filter and yis the output,
that is feature map of input at time t.
4.2.4 Comparative Analysis of Multivariate Forecast Models
This section provides a brief overview of strengths and
limitations of prediction models discusses above. Compara-
tive analysis of these models is also given here. The basic
limitation of RF is that prediction by large number of trees
make the model very complex in terms of computation and
time. Therefore, this model will be ineffective for the real-
time predictions. RF are fast to train, however the prediction
process of trained model is a time consuming process. The
scenarios where running time is important, other prediction
approaches are preferable.
DNN produce good forecasting results in presence of
enough data, big model and high computation. DNN have a
significant advantage over other predictors, that it don’t require
feature engineering (a computationally expensive process). It
is highly adaptive models towards the new problems. The
major limitation of DNN is that, it require a large amount
of data for training a good model. The training of DNN is
a very expensive in terms of time and space. The complex
most DNN models are trained for weeks with hundreds of
special machines containing GPUs (Graphics Processing Unit).
Selection of suitable training method and hyper parameters is
a difficult task (as no standard theories are present). However,
DNNs are the most suitable prediction methods for big data as
it has a great computational power [12],[13]. The conventional
prediction models cannot handle huge volume and complexity
present in big data. DNN manages memory by training models
on mini batches of training data. It make partitions of data and
train parallel on multiple processor cores. The basic features
of discussed prediction methods are shown in Table 4.
III. CRI TI CA L ANALYSIS
The comprehensive survey of recent load forecasting meth-
ods lead us to the following findings. These finding can help
in improving comprehension of load forecasting.
Critical Comment 1:Modifying of optimization algo-
rithm to converge fast may led to fall in the local optimum
and unstable solution.
Critical Comment 2:DNN are computationally expen-
sive. In process of selecting optimal network parameters,
the number of neurons in hidden layers and number of
layers, increase should be in very small successive steps.
Because both time and space complexity increase with
increase in number of layers or neurons.
Critical Comment 3:The optimization of a predictor’s
hyper parameters for a certain test dataset may lead to
over fitting on that specific dataset [10],[18],[23],[35].
This optimized model is not guaranteed to perform well
on the unseen data. Therefore, degree of optimization of
any algorithm is a matter of special care.
Critical Comment 4:For establishing any prediction
model, enough data must be fed as model input, as
the load data contains seasonality. Enough data that
cover the whole seasonality pattern should be input for
development of stable and generalized prediction model.
Critical Comment 5:The study of relevant literature of
load forecasting reveals that the forecasting of long-term
energy load is very rare. There is a lot of research scope
in the field of long-term energy forecasting as this area
is still very immature.
Critical Comment 6:Big data is not considered in most
of the analysis performed through load forecast [16]-[36].
Analysis of big data can unveil the un-precedent insights
useful for market operation planning and management.
IV. CON CL US IO N
This work is expected to serve as an initial guide for
those novice researchers, who are interested in the area of
energy consumption prediction. Particularly, energy big data
is focused in this study. Following conclusions are drawn from
this study:
1) Most of the research work is on short or medium-term
load forecasting. Long-term term load forecasting is an
area that still needs to be explored in detail.
2) There is no universal technique for electricity load
prediction and the choice of prediction models depends
on the scenario and forecast horizons.
3) It is concluded that multivariate prediction models are
suitable for large dataset, whereas, univariate predictors
perform well on small datasets.
4) Overall, deep learning prediction methods outperform all
the classic and machine learning prediction methods in
terms of accuracy. As well as, their high computational
power makes them the most suitable choice for big data
prediction and analytics, where other machine learning
methods cannot perform very well. Furthermore, DNN
has proved to be an effective method for long-term
forecasting.
5) Energy big data analytics is an emerging field. There
is a lot of research scope for novice researchers in this
area. The unprecedented insights drawn from big data
can be beneficial for energy utilities in: improving ser-
vice quality, maximizing profit, detecting and preventing
energy thefts and many other ways.
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