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Electricity Load and Price Forecasting Using Enhanced Machine Learning Techniques


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The exponential increase in electricity generation and consumption pattern are the two main issues in the wholesale markets. To handle these issues different machine learning techniques are used for load and price prediction in the research field. The wholesale utilities provide real-time data of load and price for the better prediction of electricity generation purposes. The New York Independent System Operator (NY-ISO) is one of the utility which provide electricity to different counties like United States, Canada and Israel. In this paper, hourly data of 2016–2017 is used for the forecasting process of load and price of New York City. Feature selection and extraction are used to achieve important features. The feature selection is done by two techniques Classification and Regression Tree (CART) and Recursive Feature Elimination (RFE) and Feature extraction by using Singular Value Decomposition (SVD). The Multiple Layer Perceptron (MLP), Support Vector Machine (SVM) and Logistic Regression (LR) classifiers are separately used for forecasting purposes of electricity load and price. Further enhance these three techniques EMLP, ESVM and ELR to take more accurate results for electricity load and price forecasting.
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Electricity Load and Price Forecasting
Using Enhanced Machine Learning
Hamida Bano1, Aroosa Tahir2, Ishtiaq Ali1, Raja Jalees ul Hussen Khan1,
Abdul Haseeb1, and Nadeem Javaid1(B
1COMSATS University Islamabad, Islamabad 44000, Pakistan
2Sardar Bhadur Khan Women University Quetta,
Quetta 87300, Pakistan
Abstract. The exponential increase in electricity generation and
consumption pattern are the two main issues in the wholesale markets.
To handle these issues different machine learning techniques are used for
load and price prediction in the research field. The wholesale utilities
provide real-time data of load and price for the better prediction of elec-
tricity generation purposes. The New York Independent System Operator
(NY-ISO) is one of the utility which provide electricity to different coun-
ties like United States, Canada and Israel. In this paper, hourly data of
2016–2017 is used for the forecasting process of load and price of New
York City. Feature selection and extraction are used to achieve important
features. The feature selection is done by two techniques Classification
and Regression Tree (CART) and Recursive Feature Elimination (RFE)
and Feature extraction by using Singular Value Decomposition (SVD).
The Multiple Layer Perceptron (MLP), Support Vector Machine (SVM)
and Logistic Regression (LR) classifiers are separately used for forecast-
ing purposes of electricity load and price. Further enhance these three
techniques EMLP, ESVM and ELR to take more accurate results for
electricity load and price forecasting.
1 Introduction
Electricity is the basic need of smart environment (smart city, smart industry,
smart homes, smart devices etc.). Due to the exponential increase in the demand
of electricity from different sectors (residential, commercial and industrial), it is
important to proposed a management scheme or smart system which betterly
handle the generation and consumption rate of both utility and consumption
Smart Grid (SG) is the network of transmission lines from where electricity is
efficiently transferred from the power plants to residential, commercial or indus-
trial sectors. It has a two way communication between the utility and consumers
Springer Nature Switzerland AG 2020
L. Barolli et al. (Eds.): IMIS 2019, AISC 994, pp. 255–267, 2020.
256 H. Bano et al.
Table 1. List of abbreviations
ARIMA AutoRegressive Integrated Moving Average
CART Classification and Regression Tree
DAs Data Analytics
DSM Demand Side Management
EMLP Enhanced Multilayer Perceptron
ELR Enhanced Logistic Regression
IWNN Improved Wavelet Neural Network
LR Logistic Regression
LSTM Long Short Term Memory
MLP Multilayer Perceptron
NN Neural Network
NY-ISO New York Independent System Operator
NLSSVM Normal Least Square Support Vector Machine
NSW New South Wales (Australia market)
PJM Pennsylvania, Jersey, Maryland
PV Photovoltaic
RNN Recurrent Neural Network
RFE Recursive Feature Elimination
STLF Short Term Load Forecasting
SG Smart Grid
SVM Support Vector Machine
SVD Singular Value Decomposition
however, the traditional grids have one way communication. The communica-
tion capability of SG efficiently increases its usage in the management schemes
of load and price. The SG is beneficial in many aspects quick restoration after
power outage, cost management of both utilities and consumers, reduced peak
load and improve security. Due to these beneficial factors utilities move to smart
grid to reduce the demand load in the peak hours.
Data Analytics (DA) is the broad field of qualitative and quantitative exam-
ining of large data sets. The techniques and technologies of DA are used for the
better decision making in business industries which increase the business revenue
and improve the operational efficiency.
Predictive DA is one of the types of DA which analyze the current or historical
facts to make future prediction. The utilities need to enhance the generation
and consumption rate of electricity, so that accurate prediction of load and price
of electricity is necessary. Electricity load and price forecasting techniques are
used in demand response and load management systems. Demand response is
the balance of supply side (utility) and demand side (consumer) to efficiently
control the load in peak hours (to minimize load and price).
Electricity Load and Price Forecasting Using Enhanced Machine ... 257
The utilities provide incentive programs to customers [1] to manage the
demand response. Electricity load forecasting techniques are classified into short
term, medium term and long term methods. Short term forecasting in smart
grids shows the continuous supply of electricity by saving cost. In short term
forecasting, prediction of a few minutes, hours or days ahead while in medium
term forecasting a week or month ahead and in long term forecasting prediction
of 1–10 years.
Recurrent Neural Network (RNN) is a type of Neural Network (NN) used for
the processing of sequential data. In [2], authors describe the training procedure
of LSTM network useful for the short term load forecasting. LSTM sequential
models are efficiently used for data to memorize by its memory unit for long
sequence. In paper [3], the LSTM technique improves the accuracy of model by
presenting the optimum minimization of gradient vanishing problem.
1.1 Motivation
Deep Neural network (DNN) [4] , Gated Recurrent Unit (GRU) [7], Hybrid
method + MLP [8], Convolution Neural Network (CNN), Shallow Neural Net-
work (SNN) and LSTM [9] models are efficiently used in the forecasting methods
which increase the prediction accuracy. Classifier based models are mostly used
in forecasting such as Sperm Whale algorithm + LSSVM [10]. The NN type MLP
has traditionally have one hidden layer network however the simpler models of
NN have potential to predict the accurate accuracy of price [12]. Other energy
related applications shows excellent results obtained in time series prediction
[13,14], the electricity price prediction is possible by using DL architectures.
These prediction techniques motivate the worth of the techniques to enhance
the prediction accuracy of load and price of power markets.
1.2 Problem Statement
Traditional techniques are difficult to handle big data in SGs. However, the
accurate load and price forecasting using huge amount of data from the smart
grid is the main challenges in the data analytic field. The exponential increase of
the electricity demand and the weather conditions also affect on the generation
side in terms of load and price. The high demand (on peak hours) of electricity
effect on the pricing schemes. The high complex computational time during
training process directly affect on accuracy. However, load forecasting is in [2]
ignored the computational time.
In [7], tune the hyperparameters (cost penalty, insensitive loss function and
kernal) of SVM are addressed for price forecasting however, the over fitting
problem is not handle.
1.3 Contributions
The main contribution of this paper is the time series hourly prediction of
load and price of electricity of NY-ISO market.
258 H. Bano et al.
First step to normalize the dataset in which selection and extraction techniques
are involved.
In feature selection method, two steps are used to find the relevance and
validity of data from the dataset which effect on the forecasting accuracy.
Feature Selection: CART technique is used to investigate the relationship
between a dependent variables (target) and independent variables (predictor).
It shows the strength or important impact of multiple independent variables on
dependent variables.
RFE technique is the second step of feature selection to further reduce the
dimensionality of dataset. To remove the low important features, RFE directly
improve the accuracy of prediction and reduce the model complexity.
Feature Extraction: Further reduce the high dimensionality of dataset and
computational cost SVD technique is applied.
Classifier: Three classifiers MLP, SVM and LR models are separately used for
the time series hourly prediction of load and price. Furthermore, enhance the
three techniques for more accuracy in forecasting process.
Simulation results shows that MLP, SVM and LR gives best accuracy in
electricity load and price forecasting process.
2 Related Work
Literature reviews shows that different heuristic and meta heuristic techniques
are used in electricity markets to predict load and price. The accurate forecasting
techniques are beneficial for utility companies to enhance the stability in the
market. Management of supply and demand side by reducing the cost is also
depend on the forecasting models. Long term hourly load prediction for the
period of five years by LSTM-RNN is introduced in [16]. Abedinia et al. [17]
shows the hourly solar energy load prediction of PV plant generation. In [18]
proposed a novel hybrid algorithm (ARIMA and NLSSVM) for simultaneous
prediction of load and price. The DSM schedule the connected moments of all
the shiftable devices in a smart grid to bring the load consumption curve close
to the objective load consumption curve.
In [19] hybrid model LSSVM with the combined kernal function is used for
price forecasting of Australian electricity market. A hybridize model of Phase
Space Reconstruction (PSR) with Bi-Square Kernel (BSK) regression model
called PSR-BSK model [10] . The PSR extract the evolutionary trends of power
system and the valuable features improve the forecasting procedure. The main
goal of electricity price and load prediction is to increase and decrease the power
generation of utility companies. The precise prediction of cost and load of elec-
tricity increase the competency and stability of utilities in the power markets
[20]. The medium term load and price forecasting by Multi-block NN (Elman
NN) has high capability to reduce error and improve the training mechanism of
forecast process. It also shows that the hybrid topology present an accurate pre-
diction as compared to the prediction models [21]. A day ahead price prediction
using Wavelet Packet Transform, Generalized Mutual Information for normal-
ization of data, Least Square Support Vector Machine for forecast engine and
Electricity Load and Price Forecasting Using Enhanced Machine ... 259
Artificial Bee Colony for optimization. (WRT, GMI, modified LSSVM, ABC)
in [22] Hybrid algorithm is applied on different datasets of NY-ISO, PJM and
NSW for simultaneous and accurate peak hour’s prediction of load and price and
optimizes DSM in [24]. The electricity load is predicted in microgrid scenario by
hybrid evolutionary fuzzy technique in [25]. In [26], STLF method is efficient
in managing the scheduling pattern and reducing cost of utility companies. In
[27], Wavelet Least Square Support Vector Machine (WLSSVM) model is used
to optimize by fruit fly algorithm. The summary of related work is mentioned
in Table 2.
Table 2. Summary of related work
References Fore cast
Dataset Techniques Objectives Limitations
Nord pool
spot market
Univariate and
Over fitting
Less capability
to memorize
the previous
Ontario and
IWNN Price
Over fitting
LSTM-RNN Hourly load
PV plant
Hybrid (neural
network and
Less precise
impact of
reduction on
NSW and
Not applied
on natural gas
FOA Load
260 H. Bano et al.
The proposed scheme shows the flow of the techniques which involve in the fore-
casting procedure of load and cost of electricity market. The electricity genera-
tion and consumption rate are greatly increasing by smart grids. These increasing
rates create serious problems for utility markets to manage the load and cost of
electricity changes. In the research field different machine learning techniques are
used to find the nearer predictive cost and load of electricity provider companies.
The real time electricity dataset 2016–2017 per hour are collect from NY-
ISO which is known as one of the electricity provider company and manage the
wholesale energy markets of different areas. The New York City hourly load and
cost are predict and store in a database for pre-processing. Preprocessing is a
machine learning technique which purify the dataset from redundancy, missing
values and quality of data. Data preprocessing is done by both the selection and
extraction techniques. The selection and extraction techniques are generally used
for preprocessing in machine learning techniques. The data set are divided into
training set and test set. 70% data is used for training and 30% for testing. In
the feature selection, two steps are involve by using CART and RFE techniques
to recognize the relevant and important features for forecasting purpose. SVD
technique is used for feature extraction to select the good quality of data however,
bagging is also used in extraction technique [23] . The main importance of feature
extraction is to reduce the dimensionality of dataset which directly affect the
computational time of the system. The hourly data of NY-ISO are rearranged
to compatible with the classifiers (MLP, LR and SVM) to predict the electricity
load and price. After the completion of the forecasting engine the predicted
values of load and price are evaluated by four performance metrics i.e RMSE,
MSE, MAE and MAPE. The proposed system shows the flow of forecasting
procedure of electricity load and price in Fig. 1.
Fig. 1. Proposed system model
Electricity Load and Price Forecasting Using Enhanced Machine ... 261
4 Techniques Description
In this section, all the techniques are described which are used to proposed
forecasting models for electricity load and price of NY-ISO New York market.
4.1 Data Preprocessing
The selection technique is mainly used to overcome the redundancy and
dimensionality of data which have low importance in the performance of pre-
The CART is a binary tree and information based learning algorithm. The
nodes split and the terminal leaf node contains an output which is used to
make prediction. This technique also investigate the relationship between
a dependent (target) and independent variables (predictor). It shows the
strength or importance impact of multiple independent variables on depen-
dent variables.
RFE commonly used to reduce the redundancy and discard the weak fea-
tures of data which has no effect on the electricity prediction performance.
The RFE technique also overcomes the uninformative data from the dataset
and find the prediction errors. In the algorithm the training set is used for
three purposes for predictor selection, model fitting and performance evalua-
For further extracting the interested features and to reduce the high-
dimensional data into low-dimensional data, extraction process is done by
SVD is a numerical technique based on simple linear algebra. This technique is
a factorization or decomposing a complex matrix to reduce the dimensionality
of dataset features. Classification and selection techniques improve forecasting
accuracy and simplify the classifier complexity.
4.2 Classifiers
MLP Technique
MLP is a deep learning technique and is the class of Feed-Forward Artificial
Neural Network (FFANN). It consist of three layer; input, hidden and output
layers. Each neuron has its own activation function. The input layer has no
activation function just introduced the input values. Hidden layers perform
the classification of features obtained from the input layer and the output
layer produce output through an activation function. MLP is fully connected,
each node in one layer connected with certain weights to every node in the
following layer.
The enhancement in MLP is done by tuning the parameters. Three types
of solvers ‘lbfgs, sgd and adam’ are used for weight optimization in MLP
classifier. In EMLP lbfgs solver gives better results than other two optimizers.
262 H. Bano et al.
SVM Technique
SVM is a supervised machine learning algorithm which can be used for both
classification and regression methods. The SVM linearly separable binary
sets of data. The objective to design a hyperplane in between the number
of features to classifies all training vectors in two classes. Many hyperplanes
could be chosen to separate the two classes of data points. The plane has
the maximum margin distance provides future data points can be classified.
The effectiveness of SVM depend on the high dimentional spaces of support
vectors (data points near to hyperplane) to the hyperplanes.
LR Technique
LR is a statistical method which is used for predictive analysis. When one
or more independent variables exist in the dataset the logistic function (sig-
moid function) analyze the outcomes. The sigmoid function was developed to
describe the properties of population growth. In the plots the sigmoid func-
tion is represented by S-shape curve which take any real number and map it
into a value 0 and 1. This machine learning technique can be used for both
binary or multivariate classification tasks.
Algorithm 1 MLP
1: Set MLP Network (mlp signals, weights and activation function)
2: Summation of signals with weights
3: Load and normalized the dataset
4: Splitting the dataset (training and testing)
5: Select training data
6: FOR nepochs and batchsize
7: Train the Network
9: Run Prediction using Network
10: Calculate the loss function
Algorithm 2 SVM
1: Require: X and y loaded with training labeled data, a = 0
2: partially trained SVM
3: C = some value (for example 10)
4: repeat
5: FOR (xi, yi),(xj, yj )do
6: Optimize ai and aj
8: until no change in a or other resource constraint criteria met
9: Ensure: Retain only the support vectors (ai > 0)
Electricity Load and Price Forecasting Using Enhanced Machine ... 263
5 Simulation and Results
In this paper the prediction of electricity load and price by using MLP, LR and
SVM Classifiers. The system type x64-based processor, 4.00 GB RAM and Intel
(R) core i5 processor. The implementation environment is in anaconda (spyder)
by using python language.
The four months (first two months of 2016 and 2017) hourly electricity data
of New York are taken as input from NY-ISO wholesale electricity provider. The
different features for example humidity, temperature, pressure, wind speed and
wind direction impact on the target load (TWIActualLoad) and target price
Figure 2shows the importance of features of a dataset in forecasting process.
Here we can see that the zonal transmission losses has more important role than
zonal price version in load forecasting. The features play an important role in
forecasting process, after the pre-processing of data, the normalized data of four
months is shown in Fig. 3.
Fig. 2. Feature importance of load Fig. 3. Normalized load data
Prediction of load and price of electricity market by using MLP, SVM, LR
with EMLP, ESVM and ELR techniques is shown in the following plots. The
hourly dayahead load with MLP and EMLP model and price prediction with
LR and ELR are shown in Figs. 4and 5.
Fig. 4. Dayahead load prediction Fig. 5. Dayahead price prediction
264 H. Bano et al.
The electricity load and price of first week, January 2016 and four months
of electricity load with MLP and EMLP techniques are shown in Fig. 6.
Fig. 6. a Load prediction of 1st-week, bload prediction of January, celectricity load
of four months
Electricity price prediction of week ahead and month based are shown in
Fig. 7.
Fig. 7. a Price prediction of 1st-week, bprice prediction of January, cprice prediction
of four month
5.1 Performance Evaluation
Different performance evaluators are used in this section for evaluation of the
simulation results. In particular four metrics are used, which include, Mean
Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error
(RMSE), and Mean Absolute Percentage Error (MAPE). Figures 8and 9show
the performance graph of electricity load and price data.
Fig. 8. Performance matrix of load Fig. 9. Performance matrix of price
Electricity Load and Price Forecasting Using Enhanced Machine ... 265
MAE =1
MSE =1
MAPE =100%
Table 3shows the performance evaluation comparison of different techniques
which are used for electricity load and price prediction of New York market by
using Eqs. 1–4.
Table 3. The different evaluation criterion of price and load prediction
Model Criterion Price error values Load error values
266 H. Bano et al.
6 Conclusion
In this paper, MLP, EMLP, SVM, ESVM, LR and ELR techniques are used
for the time series hourly prediction of load and price of NY-ISO electricity
market. Feature selection and extraction techniques (CART, RFE and SVD)
are used for the normalization of the dataset. These normalization techniques
remove the redundancy and irrelevant features which have less impact on the
forecasting process. The high dimensionality reduction of the dataset improved
the computation of the system and reduces cost. The LSTM and MLP models
presents clear representation of load and price forecast. In our scenario LSTM
model performs better prediction than MLP. Due to this problem, we proposed
enhanced MLP model for load and price prediction. The three techniques MLP,
SVM and LR gives 65, 64.85 and 82.62% accuracy for price prediction while
54.58, 68.71 and 67.47% for load prediction. The EMLP, ESVM and ELR models
present 76.30, 70.85, 84.52 and 79.40% and 65.01, 70.40 and 67.78% accuracy
for price and load prediction. These accurate prediction performance models
maximize the profits and formulate the long term strategies for utility companies.
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... The artificial neural network (ANN), support vector regression (SVR), and regression trees (RT) were used for the power output of photovoltaic (PV) systems [32]. For electricity load prediction, ehanced multilayer perceptron (MLP), enhanced support vector machine (SVM), and enhanced logistic regression (LR) were used [33]. Gradient boosted decision tree (GBDT), LR, random forest (RF), and MLP classifier were applied for smart grid stability prediction [34]. ...
... All these studies were not able to achieve effective results. Few studies utilized a large number of features [32,35,37] and employed a limited amount of data for modelling purposes [33,37]. Previous research did not work on class imbalance which caused insufficient results. ...
... ML algorithms can detect trends and anomalies in datasets and thus help grid system operators to make real-time decisions for better distribution of available electricity [38]. Different approaches were used for the stability prediction of power grids, but effective results were not achieved [11,[31][32][33][34][35][36][37]. To the best of our knowledge, the available literature concluded that resampling techniques were not used to balance the data for grid stability prediction. ...
Full-text available
A decentralized power grid is a modern system that implements demand response without requiring major infrastructure changes. In decentralization, the consumers regulate their electricity demand autonomously based on the grid frequency. With cheap equipment (i.e., smart meters), the grid frequency can be easily measured anywhere. Electrical grids need to be stable to balance electricity supply and demand to ensure economically and dynamically viable grid operation. The volumes of electricity consumed/produced (p) by each grid participant, cost-sensitivity (g), and grid participants’ response times (tau) to changing grid conditions affect the stability of the grid. Renewable energy resources are volatile on varying time scales. Due to the volatile nature of these renewable energy resources, there are more frequent fluctuations in decentralized grids integrating renewable energy resources. The decentralized grid is designed by linking real-time electricity rates to the grid frequency over a few seconds to provide demand-side control. In this study, a model has been proposed to predict the stability of a decentralized power grid. The simulated data obtained from the online machine learning repository has been employed. Data normalization has been employed to reduce the biased behavior among attributes. Various data level resampling techniques have been used to address the issue of data imbalance. The results showed that a balanced dataset outperformed an imbalanced dataset regarding classifiers’ performance. It has also been observed that oversampling techniques proved better than undersampling techniques and imbalanced datasets. Overall, the XGBoost algorithm outperformed all other machine learning algorithms based on performance. XGBoost has been given an accuracy of 94.7%, but while combining XGBoost with random oversampling, its accuracy prediction has been improved to 96.8%. This model can better predict frequency fluctuations in decentralized power grids and the volatile nature of renewable energy resources resulting in better utilization. This prediction may contribute to the stability of a decentralized power grid for better distribution and management of electricity.
... In [13], authors presented an analysis for day-ahead electricity price forecasting using LSTM on the Australian market in the Victoria region and the Singapore market which involved the modeling of several external variables, such as holidays, day of the week, weather conditions, and fuel prices. In [14], the authors analyzed the New York Independent System Operator through examining multiple machine-learning approaches. In [9], LSTM deep neural networks combined with feature selection algorithms for electricity price prediction of the Nordic market were used under the consideration of market coupling. ...
Conference Paper
The provision of accurate electricity price forecast is crucial for all electricity market players to encourage power generation and consumption decisionmaking. Since electricity price time sequence behavior is highly non-linear and seasonal, deep neural networks are the best model for learning non-linear data behavior for the purpose of prediction. This paper utilizes a Long-short term memory (LSTM) neural network-based model for forecasting the electricity settlement price which can successfully identity nonlinear behavior within the input data. The proposed model uses an Adam optimizer for less memory usage. ERCOT wholesale market price data along with load and temperature data have been used to illustrate the effectiveness of the proposed model. Model performance is tested, and the electricity price forecasting model is verified and validated.
... Year Model = Accuracy [16] 2022 XGBoost = 96.8% [17] 2020 SVM = 70:40% [18] 2019 XGBoost = 91:40% [19] 2019 MLP = 93:8% [20] 2018 ELM = 94:13% [21] 2018 RF = 90:01% ANN = 89:73 Table 2. A comparison of previous works ensures the stability of the grid search As demonstrated in Table.2, our study and the various models utilized exceeded earlier research. ...
The term “smart grid” refers to an innovative network for electricity distribution that employs demand-and-response and bidirectional data exchanges. Therefore, predicting the grid’s stability is crucial to make the smart grid more dependable and the electricity supply more efficient and consistent. This study’s primary objective and contribution were to develop a highly accurate XGBoost model that leverages the Genetic Algorithm as a parameters tuner to predict the stability of smart grids. The proposed model outperformed the other models (Artificial Neural Network, Random Forest, and LightGBM) with a precision of 98.02%.
... The work in [7] presented long-term forecasting of electrical loads in Kuwait using Prophet and Holt-Winters models for ten years based on the real data of historical electric load peaks. The authors of [15] also designed enhanced machine learning techniques for electricity load and price forecasting, and hourly data of one year are used for the forecasting process. In [16], the author developed a scenario-based model for the African power system using the Schwartz methodology. ...
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The increase in electricity demand is caused by population density, gross domestic product growth and technological conditions. A long-term forecast study on the electricity demand could be a promising alternative to the investment planning of power systems and distribution. In this study, the main aim is to forecast and understand the long-term electricity demand of the Taoussa area for the sustainable development of the regions of northern Mali, by using the Model for Analysis of Energy Demand (MAED) from the International Atomic Energy Agency. To fill such a knowledge gap, the long-term evolution of electricity demand is calculated separately for four consumption sectors: industry, transportation, service and household from 2020 to 2035. The demand for each end-use category of electricity is driven by one or several socioeconomic and technological parameters development of the country, which are given as part of the reference scenario (RS) and two alternative scenarios (Low and High). These scenarios were developed based on four groups of coherent hypotheses concerning demographic evolution, economic development, lifestyle change and technological change. The results showed that the annual growth rate of electricity demand in Taoussa area in all scenarios is expected to increase by only 8.13% (LS), 10.31% (RS) and 12.56% (HS). According to the seasonal variations of electricity demand, dry season electricity demand was higher than the demand in cool season during the study period. Such a conclusion demonstrates that the proposed long-term method and related results could provide powerful sustainable solutions to the electricity development challenges of Africa.
... Their LSTM model predicts only the next hour, so the whole day (24 hours) is forecasted in a recursive manner. In Bano et al. (2020) the authors study the New York Independent System Operator (NY-ISO) 1 . They compare several ML techniques using one year of hourly data (2016)(2017). ...
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Electricity prices strongly depend on seasonality on different time scales, therefore any forecasting of electricity prices has to account for it. Neural networks proved successful in forecasting, but complicated architectures like LSTM are used to integrate the seasonal behavior. This paper shows that simple neural networks architectures like DNNs with an embedding layer for seasonality information deliver not only a competitive but superior forecast. The embedding based processing of calendar information additionally opens up new applications for neural networks in electricity trading like the generation of price forward curves. Besides the theoretical foundation, this paper also provides an empirical multi-year study on the German electricity market for both applications and derives economical insights from the embedding layer. The study shows that in short-term price-forecasting the mean absolute error of the proposed neural networks with embedding layer is only about half of the mean absolute forecast error of state-of-the-art LSTM approaches. The predominance of the proposed approach is also supported by a statistical analysis using Friedman and Holm's tests.
Accurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches.
Electricity prices strongly depend on seasonality of different time scales, therefore any forecasting of electricity prices has to account for it. Neural networks have proven successful in short-term price-forecasting, but complicated architectures like LSTM are used to integrate the seasonal behaviour. This paper shows that simple neural network architectures like DNNs with an embedding layer for seasonality information can generate a competitive forecast. The embedding-based processing of calendar information additionally opens up new applications for neural networks in electricity trading, such as the generation of price forward curves. Besides the theoretical foundation, this paper also provides an empirical multi-year study on the German electricity market for both applications and derives economical insights from the embedding layer. The study shows that in short-term price-forecasting the mean absolute error of the proposed neural networks with an embedding layer is better than the LSTM and time-series benchmark models and even slightly better as our best benchmark model with a sophisticated hyperparameter optimization. The results aresupported by a statistical analysis using Friedman and Holm’s tests.
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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.
Conference Paper
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The conventional methodology for long term load forecasting is mostly restricted to electricity load data with monthly or annual granularity. This leads to forecasts with very low accuracy. In this paper, a novel method for long term load forecasting with hourly resolution is proposed. The model is fundamentally centered on Recurrent Neural Network consisting of Long-Short-Term-Memory (LSTM-RNN) cells. The long term relations in a time series data of electricity load demand are taken into account using LSTM-RNN and hence results in more accurate forecasts. The proposed model is implemented on real time data of ISO New England electricity market. Precisely, publicly available data of twelve years from 2004 to 2015 have been collected to train and validate the model. Electricity demand predictions have been made for a period of five years from 2011 to 2015 on a rolling basis. The proposed model is found to be highly accurate with a Mean Absolute Percentage Error (MAPE) of 6.54 within a confidence interval of 2.25%. Moreover, the model has a computation time of approximately 30 minutes which is favorable for offline training to forecast electricity load for a period of five years.
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and future data usually produces larger errors because the different fluctuation patterns in electricity price data show different mapping relations. A daily pattern prediction (DPP) based classification modeling approach for DAEPF is proposed to solve this problem. The basic idea is that first recognize the price pattern of the next day from the “rough” day-ahead forecasting results provided by conventional forecasting methods and then perform classification modeling to further improve the forecasting accuracy through building a specific forecasting model for each pattern. The proposed approach consists of four steps. First, K-means is utilized to group all the historical daily electricity price curves into several clusters in order to assign each daily curve a pattern label for the training of the following daily pattern recognition (DPR) model and classification modeling. Second, a DPP model is proposed to recognize the price pattern of the next day from the forecasting results provided by multiple conventional forecasting methods. A weighted voting mechanism (WVM) method is proposed in this step to combine multiple day-ahead pattern predictions to obtain a more accurate DPP result. Third, the classification forecasting model of each different daily pattern can be established according to the clustering results in step 1. Fourth, the credibility of DPP result is checked to eventually determine whether the proposed classification DAEPF modeling approach can be adopted or not. A case study using the real electricity price data from the PJM market indicates that the proposed approach presents a better performance than unified modeling for a certain daily pattern whose DPP results show high reliability and accuracy.
With the continuous development of the global electricity market, the electricity market needs the electricity price forecasting result to be accurate, and electricity price forecasting also has more profound practical significance. Accurate price forecasting can not only better reflect the operation of the electricity market but also help to make better decisions about the electricity market. However, the electricity price is affected by a variety of uncertain subjective and objective factors, as well as the constraints of the power system, making it more difficult to obtain accurate electricity price forecasting than electricity load forecasting. The kernel function occupies a very important position in a support vector machine (SVM), which is the key to the mature development of SVM theory. When using SVM for classification and regression, the appropriate kernel function is the basis and precondition for obtaining better classification and approximation effects. In this paper, a new combinatorial kernel function that combines RBF and UKF kernel functions is proposed and is applied to a least squares support vector machine (LSSVM), and based on this, a new hybrid model empirical mode (EMD)-Mixed-LSSVM is proposed to forecast electricity price. The hybrid model puts the electricity price data after the noise reduction by the EMD into the LSSVM with the combined kernel function for calculation. In this paper, we forecast the electricity price in Australia and compare the results of EMD-Mixed-LSSVM with other methods. The results show that EMD-Mixed-LSSVM can effectively improve the accuracy of electricity price forecasting.
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.
Forecasting methods are one of the most efficient available approaches to make managerial decisions in various fields of science. Forecasting is a powerful approach in the planning process, policy choices and economic performance. The accuracy of forecasting is an important factor affects the quality of decisions that generally has a direct non-strict relationship with the decisions quality. This is the most important reason that why the endeavor for enhancement the forecasting accuracy has never been stopped in the literature. Electricity load forecasting is one of the most challenging areas forecasting and important factors in the management of energy systems and economic performance. Determining the level of the electricity load is essential for precise planning and implementation of the necessary policies. For this reason electricity load forecasting is important for financial and operational managers of electricity distribution. The unique feature of the electricity which makes it more difficult for forecasting in comparison with other commodities is the impossibility of storing it in order to use in the future. In other words, the production and consumption of electricity should be taken simultaneously. It has caused to create a high level of complexity and ambiguity in electricity markets. Computational intelligence and soft computing approaches are among the most precise and useful approaches for modeling the complexity and uncertainty in data, respectively. In the literature, several hybrid models have been developed in order to simultaneously use unique advantages of these models. However, iterative suboptimal meta-heuristic based models are always used for combining in these models. In this paper, a direct optimum parallel hybrid (DOPH) model is proposed based on multilayer perceptrons (MLP) neural network, Adaptive Network-based Fuzzy Inference System (ANFIS), and Seasonal Autoregressive Integrated Moving Average (SARIMA) in order to electricity load forecasting. The main idea of the proposed model is to simultaneously use advantages of these models in modeling complex and ambiguous systems in a direct and optimal structure. It can be theoretically demonstrated that the proposed model due to use the direct optimal structure, can achieve non-less accuracy than iterative suboptimal hybrid models, while its computational costs are significantly lower than those hybrid models. Empirical results indicate that the proposed model can achieve more accurate results rather than its component and some other seasonal hybrid models.