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Short Term Load Forecasting Using XGBoost

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For efficient use of smart grid, exact prediction about the in-future coming load is of great importance to the utility. In this proposed scheme initially we converted daily Australian energy market operator load data to weekly data time series. Furthermore, we used eXtreme Gradient Boosting (XGBoost) for extracting features from the data. After feature selection we used XGBoost for the purpose of forecasting the electricity load for single time lag. XGBoost perform extremely well for time series prediction with efficient computing time and memmory resources usage. Our proposed scheme outperformed other schemes for mean average percentage error metric.
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Short Term Load Forecasting
Using XGBoost
Raza Abid Abbasi1, Nadeem Javaid1(B
), Muhammad Nauman Javid Ghuman2,
Zahoor Ali Khan3, Shujat Ur Rehman2, and Amanullah2
1COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
nadeemjavaidqau@gmail.com
2Quaid-i-Azam University, Islamabad 44000, Pakistan
3Computer Information Science, Higher Colleges of Technology,
Fujairah 4114, UAE
http://www.njavaid.com/
Abstract. For efficient use of smart grid, exact prediction about the in-
future coming load is of great importance to the utility. In this proposed
scheme initially we converted daily Australian energy market operator
load data to weekly data time series. Furthermore, we used eXtreme Gra-
dient Boosting (XGBoost) for extracting features from the data. After
feature selection we used XGBoost for the purpose of forecasting the elec-
tricity load for single time lag. XGBoost perform extremely well for time
series prediction with efficient computing time and memmory resources
usage. Our proposed scheme outperformed other schemes for mean aver-
age percentage error metric.
1 Introduction
Energy production and consumption difference minimization is a challenging
task these days. Efficient consumption of electricity is one good solution to this
problem. Researchers has done a lot of work in for introducing cost effective
and efficient energy utilization systems. Next generation Smart Grid (SG) is
the most attractive solution so far. SG is the integration of information and
communication technology in traditional grid which makes it intelligent power
grid supporting real-time information exchange between producer and consumer.
SG enables the energy efficiency optimization. More precisely, the SG needs an
accurate forecasting of the energy load for more productive application.
AN increasing attempt of deregulating strength markets to shape a more
dependable, green, and price-effective system with the aid of improving com-
petitions has been witnessed in world’s important economies [1,2]. In the lib-
eralized markets, the strength is commoditized and consequently its price is
dynamic. Due to the fee variant, pricing power correctly will become important
to generate profits, schedule strength productions, and plan load responses [3
7]. The accurate power charge forecasting is helpful to decide the strength rate
and accordingly is precious. As liberalized electricity markets include types, day-
ahead and real-time [8], it’s miles meaningful to discuss both of the day-ahead
c
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 1120–1131, 2019.
https://doi.org/10.1007/978-3-030-15035-8_108
STLF Using XGBoost 1121
and online forecasting of the power fee. The energy rate forecasting has been
vigorously studied inside the literature. From the application aspect, the fore-
casting of the energy rate in unique deregulated markets of essential economies
around the world has been stated [915].
The Rest of the paper is structured as defined next. The mostly used practi-
cle techniques for load forecasting are discussed in Sect. 2. The Sect. 3enlightens
the different problems associated with the load prediction. The proposed model
for load prediction and the evaluation metrices are explained in Sect.4. Exper-
imental results are depicted and highlighted in Sect.5. Conclusion about the
work done in this paper is expressed in Sect. 6at the end.
2 Related Work
Authors in [16] proposed a novel practical methodology using quantile regression
mean on a set of sisters point forecasts. Data from GEFCom2014 probabilistic
load track was used to forecast for developing probabilistic load forecasts. The
suggested scheme has dual advantages, where It will strength the advancement in
the field of point load forecasting, it is not dependent on the high quality expert
predictions. Scheme proposed in this work produces better results as compared to
the benchmark methods. “Recency effect” a psychology term is used by authors
in [17]. They exploited the fact that power consumption demand is influenced by
the temperature of the earlier hours. Authors produced a ample study to show
the effect of recency with the help of big data. Modern computing power is used
in order to decide how many lagged temperature are required for catching the
recency effect completely without affecting the predicting accuracy.
In [18] authors proposed a scheme for big data analytics in smart grid which
aim at reducing the electricity cost for users. Moreover they explored the individ-
ual components needed for an improved decision support system for the purpose
of energy saving. The presented framework has four different layers in its archi-
tecture, i.e., smart grid, data accumulation, an analysis counter and supporting
web portal. Future power consumption is fore-casted and optimized through a
innovative composite nature inspired meta heuristic prediction scheme. A versa-
tile optimization algorithm works as a backbone for the analytics counter that
helps in achieving accurate results. The proposed novel framework is the major
contribution of this work, which supports the energy saving decision process.
This contribution is the basis for full scale, Smart Decision Support System
(SDSS). SDSS can identify the usage pattern of an individual user which helps
in enhancing the efficiency of energy usage where improving the accuracy of fore-
casted energy demands. Authors in [19] used forecasting analytics while focusing
the extraction of related external features. More explicitly the proposed scheme
predicts the spot prices in German energy market in relation to the historical
data of prices and weather features. Least Absolute Shrinkage Selection Opera-
tion (LASSO) finds the related weather stations where implicit variable selection
is executed by Random Forest (RF). This work enhanced the prediction accuracy
with respect to Mean Average Error (MAE) by 16.9%.
1122 R. A. Abbasi et al.
A novel modeling scheme for electricity price prediction is introduced in [20].
Four different deep learning models are suggested by Lago et al. for forecast-
ing electricity prices that lead to advancement in forecasting accuracy. Authors
proposed despite the presence of a good number of electricity price forecast-
ing methods, still a benchmark is missing. This work compared and evaluated
27 different common techniques used for electricity price prediction and then
proved how the proposed models outperform the state of the art techniques
those are significant. Wang et al. used Stacked De-noising Auto-encoder and
Random Samples RS-SDA for live and next day hourly price prediction. In [21]
short term forecasting of the electricity price is performed using data driven
scheme. Deep Neural Networks type, SDA and its extended version RS-SDA are
used to forecast the electricity price hourly for the data collected from different
states of United States. This research is focused on next day hourly prediction
and the live hourly prediction. SDA defined models are assessed in comparison
with conventional neural network and support vector machine, where next day
prediction SDA models accuracy is assessed in comparison industrial model.
In [22] Lagoa et al. introduced two distinct schemes for combining market
incorporation in energy price prediction and to enhance the forecasting depic-
tion. First scheme suggested a DNN that examines features from linked markets
to enhance the forecasting results in a community market. Features importance
is calculated using a innovative feature selection scheme that contains the opti-
mization and functional analysis of variance. Second scheme forecasts the prices
from two adjacent markets simultaneously which bring the accuracy metric Sym-
metric Mean Absolute Error (SMAPE) even further lower. Raviv et al. worked
on predicting next day energy prices while utilizing hourly prices in [23]. This
work exhibit that dismantled hourly rates include handy forecasting facts for the
daily typical prices in the Nord pool market. It is evaluated that the multivari-
ate patterns for the complete group of hourly prices considerably go better than
univariate patterns of the daily normal price. Multivariate models reduce RMSE
upto 16%. In [24] authors worked on electrical load forecasting on the basis of
pre analysis and weight coefficients optimization. A novel scheme is introduced
exploiting the features of electrical load data i.e., capacity to effectively calcu-
late the seasonality and nonlinearity. The proposed new scheme can use up the
advantages stay away from disadvantages of the individual schemes. In suggested
combined scheme the data fore analyzation is adapted so that conflicts can be
minimized in the data, where weight factors are adjusted using cuckoo search
in the combined model. The newly proposed scheme outperforms the individual
forecasting models regarding forecast performance.
Singh et al. worked on the amount of power consumed prediction in [25].
An intellectual data mining scheme is proposed that can evaluate, predict and
reflect electricity time series to disclose numerous temporary energy using pat-
terns. These patterns help to identify appliance usage relationship with time
i.e., hour of day, week, month e.t.c, and appliance usage relationship with other
appliances. This identification basis for the understanding the customer usage
behavior, energy load forecasting and the price forecasting. Authors proposed
STLF Using XGBoost 1123
Bayesian network forecasting, constant analysis of data through data mining and
unsupervised data accumulating for electricity consumption prediction. In [26]
authors proposed a short-lived electricity load prediction scheme for academic
buildings. This work used 2-stage forecasting analysis for the productive working
of their energy system. Energy consumption data is collected from different uni-
versities and moving average method is used for finding the energy load pattern
according to week day. Random Forest (RF) technique is used for forecasting
the daily energy load. RF performance is assessed using cross-validation on time
series.
Gonz´alez et al. predicted electricity price adopting functional time series
using a New Hilbertian ARMAX model in [27]. Suggested scheme has a lin-
ear regression structure, where functional variables are operated by functional
parameters. Where functional parameters are fundamental entities with linearly
combined kernels as sigmoid operations. Quasi-Newton model is used for param-
eters optimization in sigmoid which minimizes the sum of squared error. Data
integrity attacks affect the results of load prediction models i.e., artificial neural
network, multiple linear regression, support vector regression and fuzzy interac-
tion regression). Authors in [28] worked on exposing the consequences of these
attacks. We begin by simulating some knowledge integrity attacks through the
random injection of some multipliers that follow a traditional or uniform distri-
bution into the load series. Then, the four same load prognostication models are
used to generate one-year-ahead ex post purpose forecasts so as to supply a com-
parison of their forecast errors. The results show that the support vector regres-
sion model is most robust, followed closely by the multiple rectilinear regression
model, whereas the fuzzy interaction regression model is that the least sturdy of
the four. withal, all four models fail to supply satisfying forecasts once the size
of the info integrity attacks becomes giant. This presents a serious challenge to
each load forecasters and therefore the broader prognostication community: the
generation of correct forecasts beneath knowledge integrity attacks.
Dong et al. worked on the energy management in a microgrid. Bayesian-
optimization-algorithm (BOA) is used for a single SG using house. Authors
in [29] articulates the enhancement beyond the closed form equitable function
equation, and work out on it using BOA based data-driven technique. We can
consider the suggested technique as a black box function improving technique
as a whole. Furthermore, it has the ability to handle the microgrid working and
argument forecasting ambiguity.
3 Motivation and Problem Statement
Electricity load prediction is an important part of advanced power systems
i.e., SG, effective power controlling, and improved energy operation engineering.
Therefore, highly accurate prediction is needed for different perspectives, that
are related to control, forwarding, planning and unit responsibility in a grid.
Artificial Intelligence (AI) centered schemes has high competency to manipu-
late complicated mathematical problems, therefore, these techniques are widely
employed in number of research areas.
1124 R. A. Abbasi et al.
The Artificial Neural Network (ANN) outperforms statistical schemes, as
ANN is more efficient in mapping inputs to the outputs beyond complicated
mathematical designs. Diverse learning structures are used by ANN for exploit-
ing the linear association among the inputs [30]. ANN schemes has better depic-
tion than analytical and time series techniques for prediction problems. The
prediction performance in neural network is enhanced by the pre-processing of
training data, high equivalence impact, optimal network structure and better
learning algorithm. Moreover, ANN brings rapid confluence, minimized comput-
ing complexity, minimal training period and improved generalization [31].
Given a time series of 30 mins electricity loads, up to the time t, X1,.....Xt,
our goal is to predict load at time t+1, i.e., Xt+1.
4 Proposed System Model
Our proposed system forecasts the electricity load. In our proposed model we
used dataset from Australian Energy Market Operator (AEMO). In this dataset
electricity load recordings are taken after every 30 min, therefore producing 48
recordings for 48 time lags in a single day. Now if we want to predict the load of
a time lag, we have just 48 features to use. To increase the number of features
and better prediction we combine the records of week days from same week to
form a single record yielding 336 recordings for a single record, i.e we have 48 ×7
features. The proposed system model is visualized in Fig. 1.
Now we calculate the importance of each feature in updated dataset using
XGBoost feature selection technique. It help us to select the most appropriate
features for selection. We consider 35–40 features with highest importance values
for training and testing of our proposed scheme.
We have 1 year of data for 12 different regions, bringing 12×365 records for
365 days in a year. We divide the data into training and testing data as 75%
training and 25% testing data. We train our proposed model using training data
and perform testing using testing data. Algorithmic steps followed during the
process of load prediction using XGBoost are defined in Algorithm 1.
Fig. 1. System model
STLF Using XGBoost 1125
Algorithm 1. Proposed Scheme Algorithm for Load Prediction.
1: Load daily records data
2: Convert daily records data to weekly records data
3: for i1tosize(features)do
4: Calculate feature importance for feature
5: end for
6: Select features with importance value greater then threshold
7: Divide data into training and testing data
8: Train model over training data
9: Predict load using trained model over testing data
10: Calculate accuracy
11: Calculate MAPE
12: Calculate MAE
4.1 Evaluation Metrices
We used various standards for the evaluation of our proposed prediction model
efficiency. The two most commonly used metrices for the measurement of predic-
tion accuracy are Mean Absolute Percentage Error (MAPE) and Mean Absolute
Error (MAE).
4.1.1 MAPE
The MAPE may be a live of prediction accuracy of a forecasting methodology for
constructing fitted statistic values in statistics, specifically in trend estimation.
it always expresses accuracy as a proportion of the error. as a result of this range
may be a percentage, it may be easier to know than the opposite statistics. The
MAPE is outlined as shown in (1). Here, and area unit the actual worth and
therefore the forecast worth, severally. Also, is the number of times discovered.
MAPE =100
n
n
t=1
AtFt
At
(1)
4.1.2 MAE
In statistics, the MAE is employed to measure however shut forecasts or predic-
tions area unit to the particular outcomes. It’s calculated by making a mean of
absolutely the variations between the prediction values and therefore the actual
ascertained values. The MAE is defined as shown in (2). Wherever nine is that
the prediction price, is the actual price.
MAE =1
n
n
i=1
|fiyi|=1
n
n
i=1
|ei|(2)
1126 R. A. Abbasi et al.
5 Simulation Results and Discussion
We evaluated the performance of our proposed forecasting technique for predict-
ing the load. We did perform a lot of experiments. The results obtained from
extensive simulation are discussed here in this section.
5.1 Data Set Description
We used AEMO load data for the year 2017. The dataset records electricity load
after every 30 mins making 48 lags daily. The considered dataset has records
about 12 different areas for the same year 2017. We considered 365 days in a
year in the provided dataset. Here Fig.2plots the load profile for individual
week days. i.e., each weekday has its own line. From the graph we can see that
Saturday has an overall highest load with respect to the other week days in the
selected week. Furthermore, we can see that Wednesday has lowest load with
respect to the other weekdays in the selected week. Where Fig.3is displaying
the load of two consecutive weeks. It is clear from Figs. 2and 3that the electricity
load data has daily as well as weekly cycles. The load at a specific time with
respect to the other day is more or less same and it rises and falls more or less
like the previous and next day. Similarly for the week, we can see that the load
at one day in a week is more or less to the same day in previous and next week.
These cycles are due to the cycles in daily human activities. For performing the
load prediction of a specific time lag we combined the daily loads for same week
to form a weekly dataset. i.e We combined the data from same weekdays to form
single row. While conversion we neglected the weekdays that were not forming
a complete week at the end of the year. This conversion provides more features
for forecasting the load of a time lag.
Fig. 2. Daily load by weekdays
STLF Using XGBoost 1127
Fig. 3. Two weeks load
5.2 Prediction Model Configurations
We used XGBoost a gradient boosting framework, introduced back in 2014.
XGBoost can be used as a forecasting technique for feature selection and load
prediction of a time lag. From prediction to classification XGBoost has proved
its worth in terms of performance.
When we convert the dataset to form a weekly data we have 48×7 recordings
for a week. One row in the modified dataset represent a week with 336 features.
To understand the importance of features, we used XGBoost to calculate the
Feature Importance (FI) of all these features.
Figure 4is depicting the FI of all these features. The greater the FI values
means the feature will more effect the load prediction. It is evident from Fig. 4
that features with hight importance values are less in number, where most of
the features have low importance value. We can see that the features close to
the predicting lag have high importance as well. Also it is evident that days
having same weekday for which we are predicting the load i.e., sunday have
high importance value. We will only use the features with high FI values for the
purpose of prediction and eliminate all other features from dataset. For selecting
the features, we set a threshold for feature importance and we set this threshold
by repeating experiment multiple times, and trying different threshold value.
The value with best results in considered as threshold. The Fig. 4shows that the
features with high FI value are less in number, it will save the running time.
5.3 Forecasting Results
We used XGBoost for forecasting the load for a specific time lag in a week using
weekly data. Figure 5shows the real load in the dataset and the XGBoost fore-
casted load. Here x-axis is depicting the time lags where y-axis is the load at
1128 R. A. Abbasi et al.
Fig. 4. XGB feature importance
that specific time lag. Actual load is represented by the blue graph, where fore
casted load is represented by the green graph. We can see that the XGBoost
load prediction follows the real load at most of the time, however at some high
load instances XGBoost is not exactly following the real load. We can see that
XGBoost is not predicting well at the high loads.
Fig. 5. XGB predictions
The XGBoost load forecasting results for a time lag are displayed in Fig.6.
We can see that XGBoost forecasting technique results in a low Mean Average
Percentage Error (MAPE), high accuracy and high Mean Average Error (MAE).
XGBoost load prediction resulted in a 10.08% MAPE, 97.21% accuracy and
88.90% MAE.
STLF Using XGBoost 1129
Fig. 6. XGB results
6 Conclusion
In this paper, we proposed a new scheme for electricity load forecasting. We con-
verted daily electricity load information into weekly load information. It increases
number of features available for predicting load for a lag variable. Then, we used
XGBoost, a recently dominant machine learning technique for time series pre-
diction, for feature selection from converted data. Once features are extracted
we train the model using XGBoost. After training we use trained model for load
prediction.
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