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Data Analytics for Short Term Price and Load

Forecasting in Smart Grids using Enhanced

Recurrent Neural Network

Muhammad Usman1, Zahoor Ali Khan2, Inam Ullah Khan3, Sakeena Javaid1and Nadeem Javaid1,∗

1COMSATS University Islamabad, Islamabad 44000, Pakistan

2CIS, Higher Colleges of Technology, Fujairah 4114, United Arab Emirates

3COMSATS University Islamabad, Lahore 54000, Pakistan

*Correspondence: nadeemjavaidqau@gmail.com, www.njavaid.com

Abstract—In this paper, an artiﬁcial neural network (ANN)

based methodology is proposed to forecast electricity load and

price. The performance of an ANN forecast model depends on

appropriate input parameters. Parameter tuning of ANN is very

important to increase the accuracy of electricity price and load

prediction. This is done using mutual information and decision

tree. After selecting best features for forecasting, these features

are given to forecasting engine working on principles of recurrent

neural network (RNN). For simulations, dataset is taken from

national electricity market (NEM), Australia. Results show that

the methodology has increased the accuracy of electricity load

and price forecast. Whereas, the error rate of forecasting is lower

than the other models for electricity load and price.

Index Terms—Smart grid, Data analytics, Artiﬁcial neural

network, Long short term memory, Forecasting, Short term

forecasting.

I. INTRODUCTION

With the passage of time, human population is increasing

signiﬁcantly with which demand of electricity also increases.

To meet such huge demand of electricity traditional grid are not

efﬁcient. Due to poor performance and increasing maintenance

cost of traditional grid electricity prices also increased. To

overcome the limitation of traditional grid smart grids (SG) are

introduced. Success of smart girds relies on the information

they gather. If SG has good quality of data then it become easy

to forecast load demand and price through different techniques.

As it is known excess electricity generation results in high

cost therefore system operators must know how much energy

is to generate during certain periods to meet the demand and

not exceed this demand signiﬁcantly. Precisely predicting

1

the

load and price can provide useful information to reduce the

generation and maintenance costs and help in planing and

operations of a SG. With the help of data analytics in SG

extracting useful information become easier this information

helps in forecasting. With the help of data analysis on big

data previously unknown hidden patterns,energy consumption

trends and other important information is extracted. The term

big data refers to huge amount of data [

1

]. Big data have some

characteristics which are refereed as 4 V’s of big data:

Volume refers to huge amount of data generated. Huge amount

of data from grid and smart meters are recorded every minute.

1Prediction and forecasting are used alternatively

Velocity refers to speed at which new data is generated. Data

is recorded a very high speed in girds having sensitive sensor.

Variety refers to different type of data like structured and

unstructured data. Data from different sensor and devices are

also recorded.

Veracity refers to accuracy or trustworthiness of the data.

In data analytics different techniques are used on data. There

are different classiﬁer for prediction which are used in ﬁeld

of data analytics to extract useful in formation. GS used these

techniques for prediction of load demand. artiﬁcial neural

network (ANN) techniques are very helpful in when dealing

when big data because they have computational power to handle

big data and it gives better results.

A. Problem Statement and Contributions

In ANN, there are many classiﬁers which are used for

forecasting. One of them is recurrent neural network (RNN),

which is usually used for text generation. The basic problem

in RNN is gradient vanishing and gradient exploding. Model

is not properly trained and layered in the beginning of the

neural network and do not give good prediction due to these

two problems [

2

]. Vanishing gradient is a problem in which

gradient based learning algorithms found difﬁculty in training

network. In gradient based learning algorithm, weights are

updated in each epoch by taking partial derivative of the error

with respect to its weight. In some cases, gradient become

too small after sufﬁcient iteration that it prevents the weight

from updation. Sometimes training completely stops due to

vanishing gradient. There is another problem in RNN that

it can not process log sequences of input and it can not be

used in deep neural networks (DNNs) because of activation

function used in RNN. Activation functions like tanh contribute

in loss of gradient [

3

]. To overcome the limitations of RNN,

an enhanced recurrent neural network (ERNN) is proposed to

address these problems in this work.

In this paper, we highlight the electricity price and load

forecasting problem. The objective of our work is to predict

the accurate load and price using data analytics on SG. To

solve this problem, we have applied RNN to forecast load

and price. RNN is a classiﬁer that uses its previous hidden

states to predict next state. There are different parameters in

2

RNN, which are used for tuning purpose. RNN is a good

classiﬁer; however, challenges are needed to overcome its

accuracy. Many techniques have been proposed over the last

years, both statistical and machine learning [

4

]-[

11

]. In this

paper, an enhanced RNN is used, which is the combination

of long short term memory (LSTM) and FFNN to increase

efﬁciency and to give better results. This paper proposes a

comprehensive load and price forecasting method based on

RNN, which can generate complete map of future demand. The

obtained results from two case studies show that the quality

of forecasting has been signiﬁcantly improved as compared to

FFNN and RNN models.

II. PRO PO SE D SYS TE M MOD EL

In this section detail description of techniques are given

which is used in the proposed model shown in Fig. 1 to forecast

load and price. As it is known NEM dataset is used in this

system. Step wise description of techniques are as follows.

A. Feature Selection and Extraction

In ﬁrst step, 2 stage feature selection, decision tree and

recursive feature elimination (RFE), is used. Decision tree (DT)

algorithm is very popular it is computationally fast. Besides

forming interpretable classiﬁcation rules it can select features

on its own. DT tells about all possible outcomes. DT helps

in determining worst, best and expected values for different

scenarios. It can also combine with other decision techniques.

In second step, conditional mutual information (CMI) is used

for feature extraction. MI is the one of the most effective

techniques for feature extraction. However, sometimes it not

easy to get accurate estimation for high dimensional data.

The optimal feature extraction, in case of MI, is done when

a feature is created from a feature set where two features

are the least important for the value of the target and have

high redundancy among each other. In RFE, most important

features are selected from feature vector by reducing feature

set model become computationally fast. RFE reduce dimension

and redundancy of features. Which is common method for

removing redundancy.

B. Enhanced Recurrent Neural Network(ERNN) Prediction

Model

Recurrent neural network (RNN) is used for price and load

forecasting. RNN are the state of the art algorithm because

it is the ﬁrst algorithm that remembers its input, due to an

internal memory. In RNN information cycle through a loop

means that hidden state of cell is also input for next cell with

its input which hepls RNN to make better decision, it takes into

consideration the current input and also what it has learned from

the input it received previously which make it perfectly suited

for machine learning problems with sequential data. There are

some limitations in RNN like vanishing and exploding gradient

due to which training of deep RNN is difﬁcult. RNN also can

not deal with long sequence of data.

For forecasting of load and price in this paper ERNN is

used. which is an enhanced form of RNN. Purpose of using

enhanced scheme is that in RNN there is a problem of vanishing

gradient and RNN can not remember long sequences of input

so predicting ahead becomes difﬁcult. In vanishing gradient

the value of gradient become to small that is does not train

the layers at the beginning properly. In LSTM there cell initial

state and hidden state initialized as zero then for every input

we call LSTM cell to which hidden state and cell state is

given with current input. Previous hidden state and current

input is combined and stored in combine then combine is given

to forget layer. In forget layer irrelevant data is removed. A

candidate layer is added using combine . This layer have values

which are added to cell state. Combine layer is also given to

input layer. This layer decide which values should be added

to new cell state. when forget layer, candidate layer and the

input layer is computed then cell state is calculated. In the end

output is calculated. point-wise multiplication of output and

new cell state gives new hidden state which is then given to

next cell of ERNN.

In ERNN, there are different gates input gate ,output gate, forget

gate and cell state. These gates determine which input is to keep

and which input is not important and need to discard. ERNN

takes input vector, previous cell state and previous hidden state

and apply different operations on it and gives output and cell

state. Different activation functions are used in ERNN like

Sigmoid and Tanh these functions help in maintaining cell

state.

III. SIMULATION RESULTS

In this section results are discussed for experiment we use

dataset with attributes and techniques as mentioned above.

In the beginning dataset is loaded into system then data is

split into training and testing. MI is applied on it which give

values to features accordingly. While predicting load it gives

more importance to scheduled generation as compare to other

features. After that Features are given to DT which tells about

the importance of each feature accordingly. After which RFE is

applied on feature and most relevant three features are selected,

which are then given to classiﬁer which training on those

feature an forecast results. Which are then compare to actual

results and performance metric are computed.

A. Dataset Description

In this proposed scheme data is taken from regional market

of National Electricity Market (NEM), Australia [

12

]. This

dataset is of a grid in New South Wales (NSW) region. NEM

dataset has many attributes from which six attributes time,

dispatch price or spot price, scheduled demand, scheduled

generation, semi scheduled generation and net import are used.

The data has the attributes: dispatch price ($/MWh), scheduled

demand (Wh), scheduled generation (MW), semi scheduled

generation (MW) and net import (MW). NEM dataset has data

of 2 days from 15 October 2018 21:00 to 17 October 2018

20:55 of NSW region in Australia. This dataset is of 5 min

intervals which has total of 576 instances. Data is split into

two parts. 75% training data is used for training and 25% is

used for testing. This means that 36 hours data is used for

training and 12 hours data is for prediction. Data is split in

same way for both load and price prediction.

B. Data Preprocessing

In data preprocessing data is loaded into system and

unnecessary columns are removed. Then data is normalize and

3

Data Set

Recursive Feature

Elimination

2 Stage Feature

selection

Feature

Extraction

Decision Tree

Recurrent Neural Network

X

Y

RNN

Input

Output

Model

Evaluation

Predicted Load

And Price

Conditional Mutual

Information

XOOXXOXO

XXXXX

XXXX

Features

Fig. 1: System Model

decimal values are converted into integers. Mutual formation

is then applied to data. MI then give importance to features

according to their correlation as shown in Fig. 2 and Fig. 3.

After which these values are given to decision tree which gives

best features from dataset as shown in Fig. 2 and Fig. 3. RFE

is then applied to feature to removes redundancy from features.

C. Load Forecasting

Selected features are then given to classiﬁer which forecast

load. Here in load forecasting, we have also compare FFNN

and RNN techniques with ERNN. Which shows that ERNN

gives better result then remaining two techniques. Results are

given in Fig. 4 and Fig. 5. These graphs show forecasting of

load for 12 hour ahead and 1 hour ahead, respectively.

D. Price Forecasting

In price forecasting we have same steps of feature selection

and feature extraction after which features are given to same

forecasting engine as given to load forecasting made by

classiﬁer is given in Fig. 6 and Fig. 7. In Fig. 6, twelve hours

TABLE I: Load Forecasting Performance Evaluators

Techniques MSE MAE RMSE MAPE

ERNN 11.7 29.15 34.26 8.89

RNN 64.48 77.65 80.30 9.10

FFNN 113.84 149.91 150.89 14.22

TABLE II: Price Forecasting Performance Evaluators

Techniques MSE MAE RMSE MAPE

ERNN 9.68 17.44 31.11 44.97

RNN 19.28 17.29 31.05 40.54

FFNN 27.00 21.45 36.74 45.08

of prediction is made after training of 36 hours from Fig. 6 it is

clear that performance of FFNN is not that much good. RNN

and ERNN is giving good prediction. In Fig. 7, one hour of

price forecasting is shown. One hour of forecasting shows that

ERNN shows better prediction then RNN and FFNN. These

graph shows that our proposed scheme performs better then

benchmark techniques.

4

Dispatch Price (

$/MWh)

Scheduled Generation (MW)

Semi Scheduled Generation (MW)

Net Import (MW)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Importance

DTC

MI

Fig. 2: MI and DT feature importance for load

Scheduled Demand (MW)

Scheduled Generation (MW)

Semi Scheduled Generation (MW)

Net Import (MW)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Importance

DTC

MI

Fig. 3: MI and DT feature importance for price

E. Performance Evaluation

To Evaluate the performance, four evaluator are used:

Root Mean Square Error (RMSE), Mean Average Percentage

Error(MAPE), Mean Square Error(MSE) and Mean Absolute

20 40 60 80 100 120 140

5 Minutes Interval

5500

6000

6500

7000

7500

8000

8500

Load (MW)

Actual

ERNN

RNN

FFNN

Fig. 4: Load Forecasting Comparison of 12 Hours

2 4 6 8 10

5 Minutes Interval

5500

6000

6500

7000

7500

8000

8500

Load (MW)

Actual

FFNN

RNN

ERNN

Fig. 5: Hour Ahead Load Forecasting Comparison

Error (MAE). MAPE has the lowest value in load forecasting;

however, in price forecasting MAPE has high values as shown

in Table I and Table II and its graphical presentation is shown

in Fig. 8 and Fig. 9.

20 40 60 80 100 120 140

5 Minutes Interval

50

100

150

200

250

300

Price ($/MWh)

Actual

ERNN

RNN

FFNN

Fig. 6: Price Forecasting Comparison of 12 Hours

5

2 4 6 8 10

5 Minutes Interval

50

75

100

125

150

175

200

225

Price ($/MWh)

Actual

FFNN

RNN

ERNN

Fig. 7: Hour Ahead Price Forecasting Comparison

MAE MSE MAPE RMSE

0

20

40

60

80

100

120

140

Error Value

ERNN

RNN

FFNN

Fig. 8: Load Forecasting Error Comparison

MAE MSE MAPE RMSE

0

10

20

30

40

Error Value

ERNN

RNN

FFNN

Fig. 9: Price Forecasting Error Comparison

IV. CONCLUSION

An efﬁcient technique is proposed to forecast load and price

of electricity using data analytics. Enhanced RNN is proposed

in this paper for short term price and load forecasting. The

proposed model consists of data preprocessing. It is done by

eliminating irrelevant features using RFE and DTC. It reduces

redundancy by MI. The proposed model trains and tests data

using LSTM. Experimental results prove the effectiveness of

proposed scheme. Performance results show that our proposed

scheme has less MAPE as compared to FFNN and RNN. NEM

data set is used for training and testing of data for forecasting.

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