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A Deep Learning Approach Towards Price Forecasting using Enhanced Convolutional Neural Network in Smart Grid


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In this paper, we attempt to predict short term price forecasting in Smart Grid (SG) deep learning and data mining techniques. We proposed a model for price forecasting, which consists of three steps: feature engineering, tuning classifier and classification. A hybrid feature selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT). To perform feature selection, threshold is defined to control selection. In addition, Recursive Feature Elimination (RFE) is used for to remove redundancy of data. In order, to tune the parameters of classifier dynamically according to dataset we adopt Grid Search (GS). Enhanced Convolutional Neural Network (ECNN) and Support Vector Regression (SVR) are used for classification. Lastly, to investigate the capability of proposed model, we compare proposed model with different benchmark scheme. The following performance metrics: MSE, RMSE, MAE, and MAPE are used to evaluate the performance of models.
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A Deep Learning Approach Towards
Price Forecasting Using Enhanced
Convolutional Neural Network
in Smart Grid
Fahad Ahmed1, Maheen Zahid1, Nadeem Javaid1(B
Abdul Basit Majeed Khan2, Zahoor Ali Khan3, and Zain Murtaza1
1COMSATS University, Islamabad 44000, Pakistan
2Abasyn University Islamabad Campus, Islamabad 44000, Pakistan
3Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
Abstract. In this paper, we attempt to predict short term price fore-
casting in Smart Grid (SG) deep learning and data mining techniques.
We proposed a model for price forecasting, which consists of three steps:
feature engineering, tuning classifier and classification. A hybrid feature
selector is propose by fusing XG-Boost (XGB) and Decision Tree (DT).
To perform feature selection, threshold is defined to control selection.
In addition, Recursive Feature Elimination (RFE) is used for to remove
redundancy of data. In order, to tune the parameters of classifier dynam-
ically according to dataset we adopt Grid Search (GS). Enhanced Convo-
lutional Neural Network (ECNN) and Support Vector Regression (SVR)
are used for classification. Lastly, to investigate the capability of proposed
model, we compare proposed model with different benchmark scheme.
The following performance metrics: MSE, RMSE, MAE, and MAPE are
used to evaluate the performance of models.
1 Introduction
Nowadays, electricity plays an important role in economic and social develop-
ment. Everything is dependent on electricity. Without electricity, our lives are
imagined to be stuck. Electricity usage areas are divided into three categories:
industrial, commercial and residential. According to [1], residential area con-
sumes almost 65% of electricity from the whole generation. In the traditional
grid, most of the electricity is wasted during generation, transmission and distri-
bution. To solve this issue, SGs are introduced. A traditional grid is converted
into SG when information, communication and technology (ICT) are integrated
into the traditional grid. SG is an intelligent grid system that manages gener-
ation, consumption and distribution of energy more efficiently than traditional
grid [2]. SG provides the facility of bidirectional communication between util-
ity and consumer. As we know, energy is the most valuable asset of this world.
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): EIDWT 2019, LNDECT 29, pp. 271–283, 2019.
272 F. Ahmed et al.
It is very necessary to utilize energy in an efficient way to increase productivity
and to decrease losses and hazards. Energy crises are present everywhere, so
industries are moving toward SG. The primary goal of SG is to keep balance
between supply side (utility) and demand side (consumer) [3]. SG fulfills, all
the demands from the consumer side and gives response to their requests. Con-
sumers send their demands to the utility through Smart Meter (SM). Hence, a
huge amount of data is collected via SM regarding the electricity consumption of
consumers. Electricity usage may vary depending upon different factors such as:
wind, temperature, humidity, seasons, holidays, working days, appliances usage
and number of occupants. Utility must be aware of the usage pattern of con-
sumer. [4], Data Analytics (DA) is a process of examining data. DA is basically
used in business intelligence, for decision making. When data analyst wants to
do an analysis of electricity load consumption and pricing trends, then they take
dataset of any specific electricity company. To maintain the load of electricity
consumption, many researchers are working on forecasting of electricity load
and price [5]. There are three types of forecasting: Short Term Load Forecasting
(STLF), Medium Term Load Forecasting (MTLF) and Long Term Load Fore-
casting (LTLF). STLF consists of time horizon from a few minutes to hours.
Day ahead is considered in STLF. MTLF contains the horizon from one month
to one year. LTLF consists of time horizon from one year to several years. Dif-
ferent researchers, used different types of time horizon for forecasting. STLF is
mostly used for forecasting, because it gives better accurate prediction results
as compared to others. Consumers can also take part in SG operations to reduce
the cost of electricity by energy preservation and shifts their consumption load
from on-peak hours to off-peak hours. Consumers can utilize energy according to
their requirements. To manage supply and demand, both residential customers
and industries require electricity price forecasting to cope with upcoming chal-
lenges [6]. Robustness, reliability, computational resources, complexity, cost of
resources are some issues however, accurate price prediction is also an important
issue [7]. When the utilization of electricity is maximum then prices are also high
[8]. The price of electricity depends on various factors, such as renewable energy,
fuel price and weather conditions etc. [9,10].
1.1 Motivation
In [11], they performed price forecasting of electricity through Hybrid Struc-
tured Deep Neural Network (HSDNN). This model is a combination of CNN
and LSTM. In this model, batch normalization is used to increase the efficiency
of training data. Authors in [12], proposed a model of Gated Recurrent Unit
(GRU) in which LSTM is used as a base model for price forecasting accurately.
In paper [13], authors predict load consumption using Back Propagation Neu-
ral Networks (BPNNs) model. Authors, used this model to reduce forecasting
errors. In [14], authors proposed a combined model of Cuckoo Search, Singular
Spectrum Analysis and Support Vector Machine (CS-SSA-SVM) to increase the
accuracy of load forecasting. In [15], authors used data mining techniques such as
k-mean and KNN algorithm for electricity price forecasting. They used k-mean
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 273
algorithm to make three clusters for weekdays and also used KNN algorithm to
divide the classified data into two patterns for the months of February to March
and April to January. After classification, a price forecasting model is developed.
The price data of 2014 is used as input and results are verified by 2015 data.
1.2 Problem Statement
We reviewed the related works in electricity price forecasting using deep learning
techniques and feature engineering.
In [11] this paper proposes an electricity price forecasting system based on
the combination of two deep neural networks, the Convolutional Neural Network
(CNN) and the Long Short Term Memory (LSTM). However, they neglect the
problem of over-fitting. Authors Ziming et al. [16], worked on price forecasting by
using the hybrid model of nonlinear regression and SVM. However, the big data
is not taken in consideration. Renewable resources, DR, and other factors are
influenced on price and load [15,17]. The price of electricity changes frequently,
that is why traditional methodologies and approaches are not suitable. We need
some enhanced methods for price predictions.
1.3 Contributions
In this paper, main goal is to predict electricity price accurately by using data
mining and deep learning techniques. To achieve this, we proposed model for
price forecasting. In this work, SVR and CNN both classifiers are used for the
prediction of price. Enhanced Convolutional Neural Network (ECNN) is used as
a proposed classifier, its results are compared with different benchmark schemes.
However, it is very hard to tune the parameters of these models according to
dataset. The contributions of this paper are summarized as follows:
Hybrid Feature Selector: Hybrid feature selector is proposed in this paper.
Overfitting: Risk of overfitting is mitigated in this model.
Grid Search and Cross Validation are used to tune the parameters of classi-
fiers, by defining the subset of parameters,
Enhance classifiers is used to increase the forecasting accuracy.
2 Related Work
Authors in [11], discussed price forecasting with the proposed model of Hybrid
Structured Deep Neural Network (HSDNN) in which the combination of CNN
and LSTM is used. The accuracy of this model is compared by performance
evaluators i.e., MAE and RMSE with different models. In [12], authors described
the prediction accuracy with the proposed model of LSTM and RNN named as
Gated Recurrent Units (GRU) compared its accuracy with benchmark models:
SARIMA, Markov chain and Naive Bayes. Rohit et al.
274 F. Ahmed et al.
In [4], authors discussed the data pre-processing steps. They have worked on
how to choose a technique for feature selection and feature extraction. These
two phases are very important in data pre-processing. Feature selection and
extraction techniques play very important role in forecasting. Pre-processing of
data is a first step in every forecasting process. Normalized data provides better
results for accuracy in forecasting. Data, which is present in raw form gives poor
result in prediction. In this work, a meta learning approach is implemented and
recommends the pre-processing technique, which shows better results.
In [17], authors proposed a model for price forecasting using Deep Learn-
ing approaches i.e. DNN as an extension of traditional MLP, hybrid LSTM-
DNN structure, hybrid GRU-DNN structure and CNN model. Wang et al. [18],
authors proposed a hybrid framework of feature selection, feature extraction and
dimensionality reduction by GCA, KPCA and also predict the price of electric-
ity through SVM. In [19], authors used Stacked Denoising Autoencoder (SDA)
and DNN models. They also compared different models including SVM, clas-
sical Neural Network and multivariate regression. Lago et al. [20], worked on
DNN to improve the predictive accuracy of a market, for feature selection. They
used Bayesian optimization and functional analysis of variance. Also proposed,
another model to perform price prediction of two markets simultaneously. Raviv
et al. [21], used multivariate models for prediction hourly price instead of uni-
variate, also mitigate the risk of overfitting by using dimensionality reduction
techniques and forecast combination. Javaid et al. [22], proposed a deep-learning
based model for the prediction of price, using DNN and LSTM. They worked
on the prediction of both price and load. In [23], authors considered a proba-
bilistic model for hourly price prediction. Generalize Extreme Learning Machine
(GELM) is used for prediction. They used bootstrapping techniques, to increased
the speed of model by reducing computational time. Abedinia et al. [24], focused
on feature selection to performed better predictions. These proposed models,
based on information theoretic criteria i.e. Mutual Information (MI) and Infor-
mation Gain (IG) for feature select. Another contribution of this paper is a
hybrid filter-wrapper approach.
In [25,26], proposed a hybrid algorithm for price and load forecasting.
Also worked on new conditional feature selection, Least Square Support Vector
Machine (LSSVM) and proposed a new modification for Artificial Bee Colony
Optimization and Quasi-Oppositional Artificial Bee Colony (QOABC) algo-
rithm. Keles et al. [27], proposed a method based on ANN. They also used
different clustering algorithms to find optimal parameters for ANN. Wang et al.
[28], proposed Dynamic Choice Artificial Neural Network (DCANN), this model
is used for day-ahead price forecasting. This model is a combination of super-
vised and unsupervised learning, which deactivates the bad samples and search
optimal inputs for a model to learn. In [29], developed a hybrid model based on
Neural Network. Authors, in [30], used Multilayer Neural Network (MLNN) for
electricity price forecasting.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 275
3 Proposed Model
In this paper, a novel price prediction model is proposed. Figure 1shows the pro-
posed model for price prediction. Proposed model is divided into four modules.
The modules of proposed models are:
1. Feature Selection,
2. Feature Extraction,
3. Grid Search and Cross Validation,
4. Price Prediction using SVR and CNN.
The individual module is further explained in the following subsections.
Fig. 1. Proposed model for price prediction
3.1 Model Overview
The accuracy of prediction is key issue in electricity price forecasting. As dis-
cussed earlier, the electricity price depends on various factors, which make train-
ing of classifiers difficult. To improve accuracy of price prediction, hybrid feature
selector (i.e., DTC and XG-boost) is used to select most relevant features. At
first, RFE is used to remove dimensionality and redundancy of data. In order to
tune parameters of classifier, GS is used along with cross validation to select best
subset of parameters. Finally, selected features and best parameters are used in
classifiers to predict electricity price.
276 F. Ahmed et al.
3.2 Feature Extraction Using RFE
RFE is used to select specified number of features from dataset. It removes
weakest feature recursively, until the specified number of features is reached.
RFE requires number of feature to select, however, it is difficult to decide in
advance that how many features are most relevant. To address this issue, cross
validation is used with RFE. Cross validation calculates accuracy of different
subsets and select the subset with highest accuracy.
3.3 Feature Selection Using XG-Boost and DT
Using XG-boost and DT, importance of all features is calculated with respect to
target, i.e., electricity price. These techniques calculate the importance of fea-
tures in vector form. The components of this vector, represents the importance of
every feature in sequence. However, we can drop features which have less impor-
tance. The fusion of Xg-boost and DT gives more accurate results. Figure 4
shows the importance of features. To control feature selection, threshold is
used. Features having importance greater than or equal to threshold are con-
sidered and rest of the features are dropped. Feature selection is performed using
Eqs. 1and 2.
Fs =Reserve if IXG[i]+IDT [i](1)
Drop if IXG[i]+IDT [i]< (2)
Where, IXG[i] represents the feature importance calculate by XG-boost,
IDT [i] is the feature importance calculated by DT. is the threshold values
for the feature selection and irepresent feature.
3.4 Tuning Hyper-parameters and Cross Validation
Tuning classifier is very important to do accurate and efficient forecasting. There
is a strong relationship between hyper-parameter and results of classifier. GS is
used to the tune parameters of classifier for higher accuracy. For this purpose,
we define subset of hyper-parameters for SVM shown in Table 1.
Table 1. Subset of parameter for Grid Search
Parameter name Parameter value(s)
kernel [‘linear’, ‘rbf’]
gamma [‘scale’, ‘auto’, 5,10,20,30,50]
epsilon [0.2, 0.02, 0.002, 0.0002]
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 277
3.5 Electricity Price Forecasting
After feature selection and parameter tuning, the processed data and the best
subset of parameters are used in SVR and CNN to forecast electricity price.
Hourly price data of two months (November and December 2016) are used to
train classifier and predicts the price for first week of January 2017. We compared
the results of first January 2017 and first week of January 2017 with actual price
of electricity of NYISO. The results of SVR are shown in Fig.5(a) and (b)
whereas, results of CNN are shown in Fig. 5(c) and (d), respectively.
4 Simulation and Results
In this section, the simulation results are discussed in details.
4.1 Simulation Environment
For simulation purpose, we implement the proposed models by using the follow-
ing python libraries i.e. Keras, Tensorflow, Sklearn, numpy and pandas. Models
are implemented on a system with Intel core i3, 8GB RAM and 500 GB storage
capacity. Two different datasets are selected for simulation. Lastly, Dataset [31]
is used as input in price prediction model, which is taken from New York Inde-
pendent System Operator (NYISO). However, dataset 2 contains hourly data of
price and electricity generation from 2016 to 2017.
Fig. 2. Result of cross validation (RFE)
278 F. Ahmed et al.
(a) 2nd January, 2017 (b) First Week of January, 2017
Fig. 3. Price prediction without parameter tuning.
4.2 Results of Price Prediction Model
The proposed model is shown in Fig. 1. NYISO dataset [31] is taken as input,
which contains 9,314 real-world records. However, for the sake of demonstration,
75 days dataset are used to train model. This dataset invariably contain approx-
imately 2000 h record, i.e., from 1st November, 2016 to 15th January, 2017. The
whole simulation process is organized as:
1. Feature extraction using RFE
2. Feature selection by combining the attributes importance calculated by XG-
boost and DT
3. Parameter tuning using cross validation and Grid Search
4. Prediction using SVR and CNN
5. Results and Comparison with real data of January 2017.
Feature Extraction: To remove redundancy and dimensionality of data, RFE
is used. Although, it is difficult to determine in advance how many features set
is required. To resolve this issue, cross validation is used with REF to select
optimal number of features. Cross validation tests every combination of features
and calculates the accuracy of each subset. The subset of features with the
highest accuracy is used for prediction. Figure 2shows the maximum accuracy
score on seven number of features.
Feature Selection: Importance of selected features are calculated by both DT
and XG-boost. By adding both importance, combined importance is calculated.
For selection of features, a threshold value is defined. Features are selected
with importance greater than or equal to threshold value. Figure 4shows the
importance of every feature. Some features have very high importance, i.e., TWI
Zonal LBMP, RTC Zonal LBMP and Load. TWI Zonal Price Version shows
very less importance as compared to others features. Most of the features have
importance greater than 0.15 and that is why we set the values of threshold to
0.15. Those features whose values are less than threshold value are dropped.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 279
Fig. 4. Feature importance for price
(a) 2nd January, 2017 (b) First Week of January, 2017
(c) 2nd January, 2017 (d) First Week of January, 2017
Fig. 5. Price prediction using SVR and CNN.
280 F. Ahmed et al.
Parameter Tuning and Cross Validation: To find the optimal set of param-
eters for the classifiers, we use the defined a set of parameters as shown in Table 1.
Using GS, every possible combination of parameters are tested by the propose
model to find optimal combination of parameters.
Price Prediction: Hourly data of November and December 2016 is used to the
train classifier. SVR and ECNN are used to predict price of electricity for first
week of January. To verify the accuracy of model, predicted price is compared
with the actual price of first week of January. The results are shown in Fig. 5(a),
(b), (c) and (d). These figures show both actual and predicted price for first
day and first week of January, 2017. Figure 5(a) and (b) shows the prediction of
classifier SVR and Fig. 5(c) and (d) reports the result of CNN classifier.
Discussion of Results: As we know, the main goal of this proposed model is
to improve the accuracy of classifier to predict price correctly. The results before
parameter tuning of classifier are shown in Fig. 3(a) and (b) are less accurate.
The MAE of prediction before parameter tuning is approximately equal to 2.83.
After feature selection, extraction and parameter tuning through GS, the results
are improved. The results after parameter tuning of SVR is shown in Fig. 5(a)
and (b). The results of CNN is shown in Fig. 5(c) and (d). After parameter
tuning, the accuracy of classifiers are improved, the MAE is reduced to 1.81 The
comparison of actual values with before and after tuning classifiers are shown in
Fig. 6(a) and (b). The value of MAE before tuning is 2.83 and after parameter
tuning the value is 1.81. MAE value is decreased then it shows that results are
improved after parameter tuning. Reducing 1% error values of MAE can save
thousands of MW of electricity.
(a) 2nd January, 2017 (b) First Week of January, 2017
Fig. 6. Comparison of predictions before and after parameter tunning.
A Deep Learning Approach Towards Price Forecasting Using ECNN in SG 281
5 Conclusion
In this research study, a new model is established to predict the price of elec-
tricity efficiently and accurately. Proposed model is consist of feature selection,
feature extraction, parameter tuning and classification. Hybrid feature selector
(hybrid of DT and XG-boost) is used to select important features for predic-
tion. For dimensionality reduction and feature extraction, RFE is used. In order
to tune the parameters of classifiers, grid search is used, which boost the clas-
sifier’s accuracy. Enhanced classifiers like CNN and SVR are used to predict
price and load is proposed models for better accuracy. The results of classifiers
is satisfactory and show better accuracy than benchmark scheme.
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... Generally two machine learning techniques are mostly used where the first one is for forecasting electricity price and the later one is for the energy systems. Most of the recent methods use different flavours of deep neural networks such as [10,11,12,13] as well as the other machine learning techniques methods such as Support Vector Machine (SVM) [14,15,16], Random Forest (RF) [17], Naive and Decision Tree [18] [19]. ...
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A smart grid is a modern electricity system enabling a bidirectional flow of communication that works on the notion of demand response. The stability prediction of the smart grid becomes necessary to make it more reliable and improve the efficiency and consistency of the electrical supply. Due to sensor or system failures, missing input data can often occur. It is worth noting that there has been no work conducted to predict the missing input variables in the past. Thus, this paper aims to develop an enhanced forecasting model to predict smart grid stability using neural networks to handle the missing data. Four case studies with missing input data are conducted. The missing data is predicted for each case, and then a model is prepared to predict the stability. The Levenberg–Marquardt algorithm is used to train all the models and the transfer functions used are tansig and purelin in the hidden and output layers, respectively. The model’s performance is evaluated on a four-node star network and is measured in terms of the MSE and R2 values. The four stability prediction models demonstrate good performances and depict the best training and prediction ability.
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The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel — SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.
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Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market.
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Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN) and the Long Short Term Memory (LSTM). In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE) and Root-Mean-Square error (RMSE) evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.
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In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show how they lead to improvements in predictive accuracy. In addition, we also consider that, despite the large number of proposed methods for predicting electricity prices, an extensive benchmark is still missing. To tackle that, we compare and analyze the accuracy of 27 common approaches for electricity price forecasting. Based on the benchmark results, we show how the proposed deep learning models outperform the state-of-the-art methods and obtain results that are statistically significant. Finally, using the same results, we also show that: (i) machine learning methods yield, in general, a better accuracy than statistical models; (ii) moving average terms do not improve the predictive accuracy; (iii) hybrid models do not outperform their simpler counterparts.
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With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine (SVM) model is proposed. Off-peak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.
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Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a local market. To measure the importance of these features, we propose a novel feature selection algorithm that, by using Bayesian optimization and functional analysis of variance, evaluates the effect of the features on the algorithm performance. In addition, using market integration, we propose a second model that, by simultaneously predicting prices from two markets, improves the forecasting accuracy even further. As a case study, we consider the electricity market in Belgium and the improvements in forecasting accuracy when using various French electricity features. We show that the two proposed models lead to improvements that are statistically significant. Particularly, due to market integration, the predictive accuracy is improved from 15.7% to 12.5% sMAPE (symmetric mean absolute percentage error). In addition, we show that the proposed feature selection algorithm is able to perform a correct assessment, i.e. to discard the irrelevant features.
A novel method for short-term electrical load forecasting using back propagation neural networks (BPNNs) is proposed for reducing the forecasting error. Conventionally, BPNN for load forecasting will have a single network structure trained by either similar day (SD) or day ahead (DA) approach. A model trained using either similar day or day ahead can only learn the characteristics of either approach. Also, a single BPNN model that incorporates both will have high complexity in its structure. The proposed sequential hybrid neural network method employs BPNNs in two stages, utilizing both similar day and day ahead. The proposed method is compared against similar day and day ahead approaches. The models are tested using hourly electrical load data from the Electric Reliability Council of Texas, Texas in USA and the Global Energy Forecasting Competition of 2012. It is observed that the proposed method showed an improvement in forecasting accuracy over the BPNN and artificial neural network-particle swarm optimization models available in literature.
The ever-increasing load demand of the residential sector gives rise to concerns such as-decreased quality of service and increased demand-supply gap in the electricity market. To tackle these concerns, the utilities are switching to smart grids (SGs) to manage the demand response (DR) of the connected loads. However, most of the existing DR management schemes have not explored the concept of data analytics for reducing peak load while taking consumer constraints into account. To address this issue, a novel data analytical demand response (DADR) management scheme for residential load is proposed in this paper with an aim to reduce the peak load demand. The proposed scheme is primarily based on the analysis of consumers' consumption data gathered from smart homes (SHs) for which factors such as-appliance adjustment factor, appliance priority index, etc. have been considered. Based on these factors, different algorithms with respect to consumer's and utility's perspective have been proposed to take DR. In addition to it, an incentive scheme has also been presented to increase the consumers' participation in the proposed scheme. The results obtained show that it efficiently reduces the peak load at the grid by a great extent. Moreover, it also increases the savings of the consumers by reducing their overall electricity bills.
The wide spread deployment of smart edge devices and applications that require real-time data processing, have with no doubt created the need to extend the reach of cloud computing to the edge, recently also referred to as Fog or Edge Computing. Fog computing implements the idea of extending the cloud where the "things" are, or in other words, improving application performance and resource efficiency by removing the need to processing all the information in the cloud, thus also reducing bandwidth consumption in the network. Fog computing is designed to complement cloud computing, paving the way for a novel, enriched architecture that can benefit from and include both edge(fog) and cloud resources. From a resources perspective, this combined scenario requires resource continuity when executing a service, whereby the assumption is that the selection of resources for service execution remains independent of their physical location. This new resources model, i.e., resource continuity, has gained recently significant attention, as it carries potential to seamlessly providing a computing infrastructure from the edge to the cloud, with an improved performance and resource efficiency. In this paper, we study the main architectural features of the managed resource continuity, proposing the foundation of a coordinated management plane responsible for resource continuity provisioning. We study an illustrative example on the performance benefits in relationship to the size of databases with regard to the proposed architectural model.