FORECASTING ELECTRICITY CONSUMPTION OF RESIDENTIAL USERS

BASED ON LIFESTYLE DATA USING ARTIFICIAL NEURAL NETWORKS

Submitted by- Sachin Meena

Abstract
Household lifestyle plays a significant role in appliance consumption. The overall effects are that, it can be a
determining factor in the healthy functioning of the household appliance or its abnormal functioning. The
rapid growth in residential consumption has raised serious concerns about limited energy resources and high
electricity pricing. The proposed 134% electricity tariff adjustment by the Electricity Company of Ghana
(ECG) in the heat of economic hardships caused by COVID-19 has raised serious public agitation in Ghana
(west Africa). The unpredictable lifestyle of residential consumers in an attempt to attain a comfortable
lifestyle and the rippling effects of population growth burden energy demand in the residential sector. This
study attempts to identify the lifestyle factors that have a great influence on household energy consumption
and predict future consumption of the household with mitigating factors to cushion the effects of high
consumption. The study is based on lifestyle data using hybrid machine learning. The hybrid model achieved
high accuracy (96%) as compared
to previous models. The hybrid model performance was evaluated using mean absolute percentage error
(MAPE), root mean square error (RMSE), and correlation coefficient (R) metrics.
Keywords: Hybrid machine learning, Lifestyle data, Energy load forecasting, Artificialintelligence

Introduction
Electricity is the backbone of every economic growth in a country undoubtedly, everything of
mankind depends on it either in direct or indirect form. The energy industry players and policy makers
have serious concerns in the amount of electricity generated and consumed at all sectors since it has an
adverse effect on development.
Globally, as a matter of urgency, policymakers consider efficient use of electrical energy by
consumers as priority in order to mitigate high electricity tariff reducing the world poverty rate
(millennium development goal)(Selamawit Mussie (AUC) et al., 2015). Energy efficiency is a
challenge for a sustainable society. The progressive
growth in population coupled with rural-urban migrations and industrialization creates high demand
for energy, putting pressure on generation. Study shows that 30% of electricity generated is consumed
by residential users.
Locally, the residential sector energy consumption stood at 4,487 , representing 40.5% of the total
final energy consumption for 2022 year ending. This figure is how- ever projected to grow by 1.9
yearly according to the report by Energy Commission of Ghana. The commission’s report further
revealed the national electricity access rate value as 85.33%.

According to the U.S energy outlook report, as discussed by Jones et al and Martin et al in their
studies, residential electricity consumption varies significantly depending on weather and lifestyle
patterns of the consumer [18], for example, that an average home in the Pacific region (consisting of
California, Oregon, and Washington) consumes 35% less energy than homes in the South-Central
region. The differences are attributed to climate conditions.
Notwithstanding the difficulties stated, identifying the particular lifestyle factor(s) that significantly
influences consumption helps in proper household energy management practices and accurate energy
demands projections.

Residential load demands projections based on lifestyle data helps in the implementation of energy
policies and programs and also helps consumers to know how their future energy consumption is
affected by their lifestyle.

In, authors have argued that the home is the largest single basic unit of electricity consumptions and
when controlled, will reduce drastically the amount of electricity consume by the residential sector.
Study by Ruan et al revealed that households in Canada consumes 1.4 million Tera-joules of energy
and this is estimated to be up by 7.2%. The study stated the residential consumption figure in Canada
as 44.6%.
There is a need for thorough investigations in to residential consumers’ lifestyle to identify the various
lifestyle factors that significantly influences residential consumption and to address these factors to
curtail the rising consumption at the residential sector. Hermann have stated in his recent study
that, domestic consumption in the household could be monitored and regulated if efficient energy
management systems (EEMS) are installed in the home. The study further argued that residential users
need to take an important decision based on their lifestyle preferences to curtail domestic
consumption.

Forecasting electrical consumption base on household lifestyle data using machine learning
algorithms is a challenging task. However, it lays a strong base for effective demand response
program and provides support in terms of maintenance, automation and timely generation meeting the
energy demands of consumers.
This current study presents a hybrid machine-learning model to accurately predict household energy
consumptions in the home using a lifestyle data gathered through a questionnaire.
The objectives of the study are tailored below:

1. To propose hybrid machine learning model to predict residential consumptions using the
household lifestyle data.
2. To identify which particular lifestyle of consumer is highly predictor of consumptions
3. To evaluate the model using accuracy and error metrics. It achieves 4.20% for MAPE with
96.0% accuracy using lifestyle data in a timeline of 40 days, which validates the efectiveness
of the proposed approach.
Accurate prediction of the mode will enhance decision making by industry players in terms of
generation, distributions and consumptions and more importantly it will be benefcial to the end-users
in energy savings.
This study is guided by the following research questions:
1. How predictable is the lifestyle data in household electrical consumptions?
2. Which particular lifestyle of the household infuences electrical load consumptions?
3. How reliable and accurate in prediction of electrical loads consumption of the model?

The purpose of this study is to investigate the household lifestyle aspects of the residential energy
consumption. Lifestyle aspects include but not limited to family patterns, occupations, marital status,
and age. The energy consumption is investigated based on the life schedules of each family member.
To limit the scope of discussion, Tamale, the capital city of northern Ghana, was selected for the case
study. The remaining sections of the current paper are organized as follows: Section "Literature
review" presents a review of the pertinent literature on electricity demand predictions. In section
"Methodology and data", we discuss the materials and methods adopted for the current study. Section
"Experimental setup" presents the outcome of the study, and discussions. Section "Conclusion"
concludes the study and outlines the direction for future studies.

Literature review
Several studies on residential energy use have been conducted by many researchers in response to
increasing energy demands by residential consumers. Articles about trends of energy use and its
relationship with household’s lifestyle attributes are reviewed in this section, indicated that residential
energy consumption is expected to keep rising alongside an increased in household appliance
ownership in Japan and across Asia countries. Their survey in eighteen (18) Western countries shows
that household appliance energy consumption is heading toward saturations. Household energy
consumption trends based on family pattern, aging society and life schedules was carried-out by Luo
et al. Family member age and its infuence on household electricity consumptions was carried out by
Lazzari et al . Their fndings were supported by Gonzalez et al who analyzed household energy
consumptions in terms of family patterns, employment status, employment sector, gender and age and
concluded that lifestyle have a signifcant efect on household energy consumptions. Chou et al study
the changes in household occupant’s behaviors in Hangzhou, China and predicted that residential
energy consumption will continue to increase in the near future due to comfort living lifestyle and
serious dependency on electrical appliances and concluded that there can be a great energy savings at
the household if occupants are educated on energy savings measures. Studies by Nti et al presented a
monthly electricity demand prediction model using a soft-computing model in Bono region (Ghana)
based on historical weather and demand data. Using a multi-layer perceptron (MLP), decision tree
(DT), and support vector machine (SVM), the researcher attained an accuracy of 95% for MLP, 67.2
for SVM, and 80.57% for DT. Zhao et al conducted a study in Tokyo using feature selection and
multivariate linear regression (MLR) techniques to predict seasonal electricity demand for households
based on end-user lifestyle data and concluded that lifestyle data is signifcant in energy demand
forecasting using household factors such as family pattern, age, and building type as independent
variables [42]. Te study fails to predict the actual energy value. Nti et al conducted a study in Sunyani
(Ghana) from three hundred and fifty (350) household to forecast residential electricity consumption
based on lifestyle data using artificial neural networks. The study achieved a good accuracy with
RMSE (0.000726) and MAE (0.000976) of the proposed model as against (RMSE = 0.08816 and
MAE = 0.06911) for support vector regression (SVR) and (RMSE = 0.0657 and MAE = 0.05714) for
decision trees (DT). With household factors such as residential location, age of family head,
employment sector of the family head, nature of employment, marital status and among others [29].
The key factors that have a great impact on total household electricity consumptions includes, income
level and population size according to Kwac et al. These are the signifcant factors that can change
household energy consumptions. Increasing per capita income is highly correlated with household
energy consumptions, however, household electricity consumption has a “U” nonlinear correlation
with urbanizations and whiles electricity pricing have great negative impact correlation with
infuencing on household energy consumptions [35]. Temperature infuence on household energy
consumptions is dependent on regional locations. Hence, for household energy consumptions, per
capita income, temperature and urbanizations all indicates nonlinear correlations on changes in
household electricity consumptions [1]. On recent study, the household electricity uses and ownership
of electrical gargets in Ireland were analyzed using logit regression (LR) on large micro-dataset by
Almahamid et al in Northern Ireland. The study revealed that the usage of space and water-heating by
a household are more crucial than electrical machines in categorizing residential energy usage.
Table 1 Summary of selected articles reviewed
Studies Variables Method(s) Location (study)

Nti et al Socio-economic factors (age, income, Artificial neural network (ANN) Ghana
family size, etc)

zhang et al Family pattern and aging society Support vector machine Japan
Edward et al Life schedules Artificial neural network China
Grolinger et al Electric gadgets logit regression (LR) Ireland
Malatesta et al Socio-economic factors ARIMA China
Kwac et al Occupant behavior Linear regression China
Chou et al Occupant behavior multivariate linear regression (MLR) Tokyo
Alhussein et al Income, temperature and popula- Regression model China
tion size

Li et al Weather and family size Markov model Japan

Almahamid et al Calendar readings Support vector machine (SVM) Asia
 Ghana energy sector demand Ghana energy sector demand
for 2021 for 2022

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Fig. 1 Ghana sector electricity demands; Source: energy commission report
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regional household electricity access

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2017 2019 2021

Fig. 2 Regional household electricity access rate (Ghana)

total communities connected to grid

Regional population access rate: = × 100
total regional population

Methodology and data

This section presents the methods adopted for the implementation of
the propose household energy consumption forecasting model based
on lifestyle data using hybrid machine learning model.

Lifestyle data acquisition

A total of 450 households were randomly selected from eight
communities within the Tamale metropolitan assembly (Northern
Ghana). Sixteen (16) set of questionnaires were developed to obtain
the lifestyle data of each participant. It was initial administered
Table 2 Questionnaire distribution
Community Community ID No. of households Percentage (%)

Sagnerigu central SC1 55 12.2

Sagnerigu west SW 2 67 14.9
Choggu east CE1 71 15.8
Choggu west CW2 74 16.4
Tamale central TC1 43 9.6
Tamale west TW2 62 13.8
Tamale south TS3 33 7.3
Tamale north TN4 45 10
Total 100 100

Table 3 Variables and abbreviations

Features Abbreviation

Family size FS
Income level IL
employment EMP
Marital status MS
Educational level EDUL
Age AGE
Type of vehicle own VT
Number of rooms NR
Gender type GT
Apartment location AL

to 10 households to fine-tune the questionnaire with their comments

in Sagnerigu district of the metropolis. The final set of questionnaires
was then administered to all selected eight communities with the help
of research assistants. Table 2 shows the distribution of the
questionnaires to the selected communities.

Data preprocessing
Basic steps were carried out on the collected data in order to prepare
it for accurate and efficient forecasting. Detecting missed information,
data cleaning, noise removal, data filtering and several other basic
methods were applied as data pre-processing steps. The best features
were selected. Important and common household lifestyles are taken
into consideration in the selection of best features. Machine learning
meth- ods are then applied on the raw data.

Features selections
Redundant or non-informative features are removed using statistical
approach from the model. The stepwise linear regression is used as a
filter to evaluate the importance of each feature predictors outside the
predictive model and subsequently models only the selected features
that passes the criterions to increase the accuracy performance
model. All features served as an input of the hybrid model. Table 3
shows the variables and its abbreviation.

Error estimation
The current study employs Percentage Error estimation method for detecting outliers
as shown in Fig. 3. With a tolerance level of 10% as a condition, any percentage error
greater than the tolerance value is considered as an outlier. However, tolerance is
subjected to change depending on set criteria and the type of data.

Stage one

Establishing ARIMA based Percentage

Household ARIMA Error Correction
consumption
lifestyle prediction estimation of outliers
forecasting
model for outlier
detection

Second stage

Corrected Forecasting SVM model Forecasting

ARIMA based Household
consumpt ARIMA structure Residual
consumption consumption
ion data Residual Errors
forecasting forecasting
Errors

Fig. 3 Propose hybrid model flowchart

Evaluation metrics
The error metrics discussed in [8] are used to evaluate our model,
1. Root Mean Square Error (RMSE): It estimates the residual
between the actual and the predicted values. A smaller RMSE value
indicates a better performance model. While an RMSE value equal
to zero indicates a perfect fit of the model. This is deter- mined using
the formula below

2. The Mean Absolute Percentage Error (MAPE) discussed in [8] is

also used to meas- ure the performance of the proposed model. It is
an aggregative indicator commonly used in power systems. It is mostly
used to evaluate the forecasting performance of the whole predicting
process comprehensively. It indicates an average of the absolute per-
centage error, however the lower the value of the MAPE, the better
the performance of the model. This is determined using the formula
below

Where tv is the actual value, yv is the forecast value, M is the mean

absolute percentage error and the n is the number of times the
summation iteration happens.

3. The correlation coefficient (R): This criterion reveals the strength

of relationship between actual values and predicted values. The
correlation coefficient has a range from 0 to 1, where higher R means
it has an excellent performance measure.

average values of tv and yv respectively and tv is the actual value, yv is

the predicted value produced by the model, and m is the total number of
observations.
Experimental setup
The obtained features out of the administered questionnaires were
abbreviated as indi- cated in Table 3. The household lifestyle feature
serves as an input parameter whiles the actual monthly consumption of
participants from the Northern Electricity Company of Ghana (local
supply authority) serves as output target. The qualitative response
from the respondents are coded using the dummy variables. After
which all the 450 received responses were queued with Microsoft
excel in to comma-separates values (CSV) file format. The
implementation of the proposed model was carried in Shinny App.
Shiny app is a package in R programming language that makes it
easy to build an interactive web application straight from R
programming framework. The ARIMA model is imple- mented to
forecast the household consumption as follows. Firstly, the
household life- style is transferred in to a stationary time series by
periodic difference transformation and a first-order difference
transformation. Secondly, it is confirmed as ARIMA (2, 6, 3) through
order determination and the values of parameters are obtained
through param- eter estimation. Finally, we use the confirmed
ARIMA model to forecast the household consumption. The methods
are repeated to obtain the deviation and forecasting data.

To improve the prediction accuracy, the SVM model is use to extract the
sensitive com- ponent of the deviation. The deviation sample is use to
train the SVM model with expo- nential kernel function in order to
correct the deviation. Far-smallness and near bigness theory and
similarity theory are used to construct the input sample, since SVM
param- eters have serious effect on prediction accuracy, the
appropriate values determined by test are C= 22, ǫ = 0.18 and σ = 1.
This give the SVM high generalization. Hence we are able to predict the
household consumption and the deviations. The overall forecast
includes both the forecast for household consumption and the
deviations forecast. Fig- ure 3 shows the flowchart of the implemented
hybrid model. The outcome of the pro- posed hybrid model was
benchmarked with previous study.

Results and analysis

This section presents the results and analysis of the propose ARIMA-
SVM prediction hybrid framework for forecasting electricity
consumption based on household users life- style characteristics.
Features selection analysis
Features with significant weight of more than 0.5 are selected whiles
features with weight less than 0.5 are considered unimportant and
rejected. The features ranking index in Fig. 4 reveals that the family
size of the household highly determine its electrical energy
consumption. This affirm the study in [7] that reveals a direct
relationship between fam- ily size and electrical energy consumption.
The family size includes the nuclear family (children and parents), the
extended family and domestic staffs in the households. Fol- lowed by
income level, vehicle type, educational level, age, employment and
marital sta- tus. Features such as apartment location, number of rooms
and gender type have low correlation with energy consumption. They
are treated as unimportant features and sub- sequently rejected for
further analysis.
The results affirms study by [17, 39] that age of a household is a
significant factor of the household electricity consumption. The results
reveals a link between the type of vehicle owned and income level. This
can be argued as the monthly income of the house- hold determines the
type of vehicle owned and also the number of electrical appliances the
household uses. In simple terms, the socio-economic status of a
household and

Features ranking index

AL
NR
VT
EDUL
EMP
IL
AGE
GT
MS
FS
-0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1

Fig. 4 Features ranking index

the country at large determines the amount of electrical energy
consumed. The results affirm the findings reported in [21, 21], it
however disagrees with the findings in [8] that reported that there is no
correlation between electrical energy consumption and house- hold
income level.
This current study reveals the family size as the top-highest
significant feature. This can be interpreted that government agencies
responsible for energy sector can project electrical energy demands
based on residential population and again adequate measures, policies
and programs can be designed to mitigate the growing demands of
residential energy consumption.

Predictor’s performance evaluation on household consumption

From the features ranking index in Fig. 4, the seven (7) selected features
are further analyzed using multiple predictive accuracy measures to
assess the individual performance on household electrical energy
consumption. With the seven selected predictors, there are 128
possible models ( 27 = 128). All the 128 models are fitted and the
results are
summarize in Table 4. A “1” indicates that the predictor was included in the model
and a
“0” means that the predictor was not included in the model.
The first row (Table 4) shows the measures of predictive accuracy
for a model includ- ing all seven predictors. Applying the actual
household consumption, the CV, BIC, AIC, AICc, and adjusted R2 are
calculated using the CV function.
CV (fit.comsMR)

# CV AIC AICc BIC Adj R2

# 0.1163 −409.2980 −408.8314 −389.9114 0.7486
 Table 4 Summaries of predictors’ performances on household consumption.

EDU Age MS FS VT IL EMP CV AIC AICc BIC ADJR2

1 1 1 1 1 1 1 0.116 -409.3 -408.8 -389.9 0.749
1 1 1 1 1 1 0 0.116 -408.1 -407.8 -391.9 0.746
0 1 1 1 1 1 1 0.118 -407.5 -407.1 -391.3 0.745
1 1 0 1 1 1 1 0.129 -388.7 -388.5 -375.8 0.716
1 0 1 1 1 1 1 0.278 -243.2 -242.8 -227 0.386
1 1 1 0 1 1 1 0.283 -237.9 -237.7 -225 0.365
1 1 1 1 0 1 1 0.289 -236.1 -235.9 -223.2 0.359
1 1 1 1 1 0 1 0.293 -234.4 -234 -218.2 0.356
0 1 1 1 1 1 0 0.300 -228.9 -228.7 -216 0.334

1 1 0 0 0 1 1 0.303 -226.3 -226.1 -213.4 0.324

1 0 0 0 0 0 1 0.306 -224.6 -224.4 -211.7 0.318
1 0 0 0 0 0 0 0.314 -219.6 -219.5 -209.9 0.296
0 0 0 0 0 0 1 0.314 -217.7 -217.5 -208 0.288
0 1 0 0 0 0 0 0.372 -185.4 -185.3 -175.7 0.154
0 0 0 0 1 0 0 0.414 -164.1 -164 -154.4 0.092
0 0 0 0 0 1 0 0.432 -155.1 -155 -148.6 0.062
0 0 1 0 0 0 0 0.447 -147.3 -147 -139.2 0.054
0 0 0 1 0 0 0 0.455 -139.1 -139 -127.1 0.049
0 0 0 0 0 0 0 0.485 -125.2 -124.9 -110.6 0

These values are compared against the corresponding values from

other models. For the CV, BIC, AIC and AICc measures, model with
lowest value is selected whiles adjusted R2 the model with highest
value is selected. The results have been sorted according to the AICc
values. The best model contains all the predictors. However, a closer
look at the results reveals the individual strengths of the predictors.
The fam- ily size (FS) had the highest significant impact on
household consumption followed by marital status (MS). The results
further reveals that the Employment and Education predictors are
highly (negatively) correlated. This can mean that most of the
predictive information in Employment is also contained in the
Education variable. Also the first two rows have almost identical
values of CV, BIC, AIC and AICc. So the Employment variable could
possibly be dropped and get similar forecasts.
Result and comparison
The outcome of the hybrid model is compared with the results of
Vinagre et al, Mahia et al, Yu et al, Zogaan et al and Atalay et al. for
Vinagre et al. [36], the study employed support vector machine
(SVM) to forecast residential consumption. The SVM was
implemented on different framework (R and MATLAB). The best
result achieved with the SVM presents an average error of 6.6% when
implemented in R, it however raised to 7.0% in MATLAB software.
They concluded that both frameworks can accurately predict
residential consumption but the MATLAB is less consistent as
compared to R. This current study used the R programming
framework to implement the hybrid model. Mahia et al. [24] study
employed ARIMA model with three set of parameters ( ARIMA
(1,1,1), ARIMA(1,1,2) and ARIMA (1,1,7) ) to forecast residential
consumption on two different dataset. Their experimental results
shows that ARIMA (1, 1, 1) had high preci- sion and stable predictions
on both datasets. Yu et al. (Yu et al., n.d.) study implemented a Decision
Tree (DT) to predict household consumption. A total of 100 trees was
gener- ated with a data sample of 247. This was implemented in
MATLAB framework achiev- ing an accuracy of 91%. Zogaan et al.
[43] used random forest with one iteration with backward elimination
in R package statistical model to forecast residential consumption
achieving good accuracy of prediction. Atalay et al. [5] implemented
ARIMA–RF model. Comparing the results between our proposed
method and previously reported methods shows that our proposed
model have outperformed the previous models using the R2, RMSE
and MAPE as summarized in Table 5.

Table 5 Comparison of the propose hybrid model result with previous models results
Model RMSE MAPE (%) R2

DT 40.23 6.12 0.5766

RF 38.94 5.40 0.5841

SVM 38.77 4.87 0.5991

ARIMA 43.49 5.16 0.5988

ARIMA-RF 37.63 4.75 0.6967

ARIMA-SVM (propose work) 32.75 4.20 0.7385

 140

120

100

80

Kwh
60

40

20

0
CT1 CT2 CT3 CT4 CT5 CT6 CT7 CT8 CT9 CT10
household

RF DT SVM ARIMA ARIMA-SVM ARIMA-RF Actual value

Fig. 5 Comparison of models performance and actual household consumption

Table 6 Comparison of ARIMA-SVM model on outlier detection

s/n Method MAPE (%)

Phase-2 ARIMA-SVM (with outlier detection) 4.20

Phase-1 ARIMA-SVM (without outlier detection) 6.15

It is clear from Table 5, there are improvement in the evaluation

metrics using the hybrid model. The MAPE has reduced 4.20% with
an improvement in correlation coefficient 0.7385 and RMSE value
32.75.

Models performance and actual household consumption value comparison

Again, comparing the predicted values of each model against the
actual consumption of the household (Fig. 5) shows that our proposed
ARIMA-SVM hybrid model out performed the ARIMA-RF, DT, RF,
SVM and ARIMA models. The result can be interpreted that, the
residential users can effectively manage their electrical
consumption based on the lifestyle they choose to live and also offer
them a better opportunity to properly plan their budgets to meet their
energy demands to prevent any unforeseen shortage of electricity.

Impact of outlier detection on ARIMA‑SVM mode

The performance of ARIMA-SVM hybrid model was evaluated on two-
phase. Phase-1; ARIMA-SVM without outlier detection and Phase-2;
ARIMA-SVM with outlier detection. In phase-2, the percentage error
outlier detection method was employed with tolerance value of 10%.
The outlier detected using the percentage error method will give the
least MAPE value of 5.4%. The SVM is trained with the sample with
exponential ker- nel function to correct the deviations. The
performance of phase-1 is compared with phase-2 to evaluate the
impact of outlier detection in using the percentage error method on
ARIMA-SVM model. Table 6 summarizes the result of the ARIMA-
SVM performance with or without the outlier detection method.
It is clear from Table 6 that the overall forecasting
ability has improved. This can be interpreted that the ARIMA and
SVM has mutually supplemented each other with their individual
advantages in the hybrid model.

Conclusion
In this study, we conclude that the improvement of electricity
management system from demand side can be achieved efficiently
and effectively by using a combination of a novel data mining
techniques such as ARIMA and SVM model if household lifestyle
data is relied -on despite having a conventional ordinary electricity
grid using electricity prepaid meters. The experimental setup with
450 household data randomly selected from eight communities
within the Tamale metropolitan assembly (Northern Ghana)
revealed the family size as the most influencing predictor of
household electrical consumption. The model’s accuracy (96.0%)
as compared to previous models indicates that the proposed hybrid
model can effectively predict household electrical consumption.
The use of actual household consumption (Kwh) as target variable
in the current study makes it independ- ent on the influence of
socio-economic factors since the actual unit of household electri-
cal consumption depend on household energy management
lifestyles. One of the most popular method used for forecasting
is the ARIMA technique.
ARIMA method will provide a better forecasting accuracy as it
requires historical load of the household and fewer assumptions but
however, the household consumption is influence by other factors
such as the lifestyles of the household. The artificial intelli- gence
techniques will incorporate these factors which can improve the
accuracy further. In this current study, hybrid methodology is
employed using the ARIMA-SVM. The ARIMA is used to predict
the household consumption based on the household lifestyle
characteristics and then the SVM is used to improve the accuracy
by correcting outliers. In this study, the percentage error method is
used to detect the outliers in the household lifestyle data. Through
the experimental setup, the result shows that the hybrid model is far
much better than the individual ARIMA and SVM models in
standalone implementation. Even though the hybrid model had
achieve good accuracy measure, we believe it can be improved by
adding other parameters such as household appliance and
technology parameters.
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