No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Electric Load Forecasting using EEMD and Machine Learning Techniques
Background
Load forecasting is a crucial element in power utility business load forecasting and has influenced key decision-makers in the industry to predict future energy demand with a low error percentage to supply consumers with load-shedding-free and uninterruptible power. By applying the right technique, utility companies may save millions of dollars by using load prediction with a lower proportion of inaccuracy.
Aims
This study paper aims to analyse the recently published papers (using the New York Independent System Operator's database) on load forecasting and find the most optimised forecasting method for electric load forecasting.
Methods
An overview of existing electric load forecasting technology with a complete examination of multiple load forecasting models and an in-depth analysis of their MAPE benefits, challenges, and influencing factors is presented. The paper reviews hybrid models which are created by combining two or more predictive models, each offering better performance due to their algorithm's merits. Hybrid models outperform other machine learning (ML) approaches in accurately forecasting power demand.
Result
Through the study it is understood that hybrid methods show promising features. Deep learning algorithms were also studied for long-term forecasting.
Conclusion
In the future, we can extend the study by extensively studying the deep learning methods.
Smart Grid (SG) is a modernized grid that provides efficient, reliable and economic energy to the consumers. Energy is the most important resource in the world and almost everything relies on it. As smart devices are increasing dramatically with the rapid increase in population,
there is a need for an efficient energy distribution mechanism. Furthermore, the forecasting
of electricity consumption is supposed to be a major constituent to enhance the performance
of the SG. Various learning algorithms have been proposed in the literature for efficient load
and price forecasting. However, there exist some issues in the proposed work like increased
computational complexity. The sole purpose of the work done in this thesis is to efficiently
predict electricity load and price using different techniques with minimum computational
complexity. Chapter 1 provides an introduction of various concepts present in the power
grids. Afterwards, the unified system model, different sub-problems and the contributions
made in the thesis are also presented. Chapter 2 discusses the existing work done by different
researchers for performing electricity load and price forecasting. In Chapter 3, Enhanced
Logistic Regression (ELR) and Enhanced Recurrent Extreme Learning Machine (ERELM)
are proposed for performing short-term load and price forecasting. The former is an enhanced
form of Logistic Regression (LR); whereas, the weights and biases of the latter are optimized
using Grey Wolf Optimizer (GWO). Classification And Regression Tree (CART), Relief-F
and Recursive Feature Elimination (RFE) are used for feature selection and extraction. On the
basis of selected features, classification is performed using ELR. Moreover, cross validation
is done using Monte Carlo and K-Fold methods. In order to ensure optimal and secure
functionality of Micro Grid (MG), Chapter 4 focuses on coordinated energy management of
traditional and Renewable Energy Sources (RES). Users and MG with storage capacity are
taken into account to perform efficient energy management. A two stage Stackelberg game is formulated. Every player in the game tries to increase its payoff, and ensure user comfort and system reliability. Furthermore, two forecasting techniques are proposed in order to forecast Photo-Voltaic Cell (PVC) generation for announcing optimal prices. Both the existence and uniqueness of Nash Equilibrium (NE) for the energy management algorithm are also
considered. In Chapter 5, a novel forecasting model, termed as ELS-net, is proposed. It is a combination of an Ensemble Empirical Mode Decomposition (EEMD) method, multi-model
Ensemble Bi Long Short Term Memory (EBiLSTM) forecasting technique and Support
Vector Machine (SVM). In the proposed model, EEMD is used to distinguish between linear
and non-linear Intrinsic Mode Functions (IMFs). EBiLSTM is used to forecast the non-linear
IMFs and SVM is employed to forecast the linear IMFs. The usage of separate forecasting
techniques for linear and non-linear IMFs decreases the computational complexity of the
model. In Chapter 6, a novel deep learning model, termed as Gated-FCN, is introduced for
short-term load forecasting. The key idea is to introduce automated feature selection and
a deep learning model for forecasting, which includes an eight layered FCN (FCN-8). It
ensures that hand crafted feature selection is avoided as it requires expert domain knowledge.
Furthermore, Gated-FCN also helps in reducing noise as it learns internal dependencies
as well as the correlation of the time-series. Enhanced Bidirectional Gated Recurrent Unit
(EBiGRU) model is dovetailed with FCN-8 in order to learn temporal long-term dependencies
of the time-series. Furthermore, weight averaging mechanism of multiple snapshot models is
adapted in order to take optimized weights of BiGRU. At the end of FCN-8 and BiGRU, a
fully connected dense layer is used that gives final prediction results. The simulations are
performed and the results are provided at the end of each chapter. In Chapter 3, the simulations
are performed using UMass electric and UCI datasets. ELR shows better performance with
the former dataset; whereas, ERELM has better accuracy with the latter. The proposed
techniques are then compared with different benchmark schemes. The comparison is done
to verify the adaptivity of the proposed techniques. The simulation results show that the
proposed techniques outperform the benchmark schemes and increase the prediction accuracy
of electricity load and price. Similarly, in Chapter 4, simulations are performed using Elia,
Belgium dataset. The results clearly show that the proposed game theoretic approach along
with storage capacity optimization and forecasting techniques give benefits to both users and
MG. In Chapter 5, simulations are performed to examine the effectiveness of the proposed
model using two different datasets: New South Wales (NSW) and Victoria (VIC). From the
simulation results, it is obvious that the proposed ELS-net model outperforms the benchmark
techniques: EMD-BILSTM-SVM, EMD-PSO-GA-SVR, BiLSTM, MLP and SVM in terms
of forecasting accuracy and minimum execution time. Similarly, the simulation results
of Chapter 6 depict that Gated-FCN gives maximum forecasting accuracy as compared to
the benchmark techniques. For performance evaluation of the proposed work, different
performance metrics are used: Mean Absolute Percentage Error (MAPE), Mean Absolute
Error (MAE), Mean Squared Error (MSE) and Root Mean Square Error (RMSE). The overall
results prove that the work done in this thesis outperforms the existing work in terms of
electricity load and price forecasting, and computational complexity.
Instead of planting new electricity generation units, there is a need to design an efficient energy management system to achieve a normalized trend of power consumption. Smart grid has been evolved as a solution, where Demand Response (DR) strategy is used to modify the consumer's nature of demand. In return, utilities pay incentives to the consumer. This concept is equally applicable on residential and commercial areas; however, the increasing load demand and irregular electricity load profile in residential area have encouraged us to propose an efficient home energy management system for optimal scheduling of home appliances. Whereas, electricity consumers have stochastic nature, for which nature-inspired optimization techniques provide optimal solutions. However, these optimization techniques behave stochastically according to the situation. For this reason, we have proposed different optimization techniques for different scenarios. The objectives of this thesis include: reduction in electricity bill and peak to average ratio, minimization of waiting time to start appliances (comfort maximization) and minimization of wastage of surplus energy by exploiting the coordination among appliances and homes. In order to meet the electricity demand of the consumers, the energy consumption patterns of a consumer are maintained through scheduling the appliances in day-ahead and realtime bases. It is applicable by the defined fitness criterion for the proposed hybrid bacterial foraging genetic algorithm and hybrid elephant adaptive cuckoo search optimization techniques, which helps in balancing the load during On-peak and Off-peak hours. Moreover, the concept of coordination and coalition among home appliances is presented for real-time scheduling. The fitness criterion helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of the appliance. A multi-objective optimization based solution is proposed to resolve the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. Two optimization techniques: binary multiobjective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms are proposed to obtain the Pareto front.
The main objective of DR is to encourage the consumer to shift the peak load and gets
incentives in terms of cost reduction. However, prices remain the same for all the users even if they shift the peak load or not. In this thesis, Game Theory (GT) based Time of Use
pricing model is presented to define the pricing strategy for On-peak and Off-peak hours.
The price is defined for each user according to the utilized load using coalitional GT. Further, the proposed pricing model is analyzed for scheduled and unscheduled load. In this regards, Salp swarm and rainfall algorithms are used for scheduling of appliances and an aggregated fitness criterion is defined for load shifting to avoid the peak rebound effect.
We also proposed the coordination and coalition based Energy Management System-as-a-
Service on Fog (EMSaaS_Fog). With the increase in number of electricity consumers, the
computational complexity of energy management system is becoming a threat for efficiency of a system in real-time environment. To deal with this dilemma, the utility shifts computational and storage units on cloud and fog. The proposed EMSaaS_Fog effectively handles the coalition among the apartments within a building to maintain balance between the demand and supply. Moreover, we consider a small community, which consists of multiple smart homes. Microgrid is installed at each residence for electricity generation. It is connected with the fog server to share and store information. Smart energy consumers are able to share detail of excess energy with each other through the fog server.
With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.
With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this study, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V’s. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future.
With the increase in energy demand, extreme climates have gained increasing attention. Ice disasters on transmission lines can cause gap discharge and icing flashover electrical failures, which can lead to mechanical failure of the tower, conductor, and insulators, causing significant harm to people's daily life and work. To address this challenge, an intelligent combinational model is proposed based on improved empirical mode decomposition and support vector machine for short-term forecasting of ice cover thickness. Firstly, in light of the characteristics of ice cover thickness data, fast independent component analysis (FICA) is implemented to smooth the abnormal situation on the curve trend of the original data for prediction. Secondly, ensemble empirical mode decomposition (EEMD) decomposes data after denoising it into different components from high frequency to low frequency, and support vector machine (SVM) is introduced to predict the sequence of different components. Then, some modifications are performed on the standard SVM algorithm to accelerate the convergence speed. Combined with the advantages of genetic algorithm and tabu search, the combination algorithm is introduced to optimize the parameters of support vector machine. To improve the prediction accuracy, the kernel function of the support vector machine is adaptively adopted according to the complexity of different sequences. Finally, prediction results for each component series are added to obtain the overall ice cover thickness. A 220 kV DC transmission line in the Hunan Region is taken as the case study to verify the practicability and effectiveness of the proposed method. Meanwhile, we select SVM optimized by genetic algorithm (GA-SVM) and traditional SVM algorithm for comparison, and use the error function of mean absolute percentage error (MAPE), root mean square error (RMSE) and mean absolute error (MAE) to compare prediction accuracy. Finally, we find that these improvements facilitate the forecasting efficiency and improve the performance of the model. As a result, the proposed model obtains more ideal solutions and has higher accuracy and stronger generalization than other algorithms.
Load demand forecasting is a critical process in the planning of electric utilities. An ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep learning approach is presented in this work. For this purpose, the load demand series were first decomposed into several intrinsic mode functions (IMFs). Then a Deep Belief Network (DBN) including two Restricted Boltzmann Machines (RBMs) was used to model each of the extracted IMFs, so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of all IMFs can be combined by either unbiased or weighted summation to obtain an aggregated output for load demand. The electricity load demand data sets from Australian Energy Market Operator (AEMO) are used to test the effectiveness of the proposed EMD-based DBN approach. Simulation results demonstrated attractiveness of the proposed method compared with nine forecasting methods.
Ensuring sustainability demands more efficient energy management with minimized energy wastage. Therefore, the power grid of the future should provide an unprecedented level of flexibility in energy management. To that end, intelligent decision making requires accurate predictions of future energy demand/load, both at aggregate and individual site level. Thus, energy load forecasting have received increased attention in the recent past, however has proven to be a difficult problem. This paper presents a novel energy load forecasting methodology based on Deep Neural Networks, specifically Long Short Term Memory (LSTM) algorithms. The presented work investigates two variants of the LSTM: 1) standard LSTM and 2) LSTM-based Sequence to Sequence (S2S) architecture. Both methods were implemented on a benchmark data set of electricity consumption data from one residential customer. Both architectures where trained and tested on one hour and one-minute time-step resolution datasets. Experimental results showed that the standard LSTM failed at one-minute resolution data while performing well in one-hour resolution data. It was shown that S2S architecture performed well on both datasets. Further, it was shown that the presented methods produced comparable results with the other deep learning methods for energy forecasting in literature.
In this paper, for the first time, an ensemble of deep learning belief networks (DBN) is proposed for regression and time series forecasting. Another novel contribution is to aggregate the outputs from various DBNs by a support vector regression (SVR) model. We show the advantage of the proposed method on three electricity load demand datasets, one artificial time series dataset and three regression datasets over other benchmark methods. ANN artificial neural network FNN feedforward neural network RNN recurrent neural network CNN convolutional neural network ENN ensemble feedforward neural network SVM support vector machine SVR support vector regression DBN deep belief network RBM restricted Boltzmann machine SAE stacked autoencoder MAPE mean absolute percentage error RMSE root mean square error ARMA auto-regressive moving average ARIMA auto-regressive integrated moving average
We evaluate 179 classifiers arising from 17 families (discriminant analysis, Bayesian, neural networks, support vector machines, decision trees, rule-based classifi ers, boosting, bagging, stacking, random forests and other ensembles, generalized linear models, nearestneighbors, partial least squares and principal component regression, logistic and multinomial regression, multiple adaptive regression splines and other methods), implemented in Weka, R (with and without the caret package), C and Matlab, including all the relevant classifiers available today. We use 121 data sets, which represent the whole UCI data base (excluding the large-scale problems) and other own real problems, in order to achieve significant conclusions about the classifier behavior, not dependent on the data set collection. The classifiers most likely to be the bests are the random forest (RF) versions, the best of which (implemented in R and accessed via caret) achieves 94.1% of the maximum accuracy overcoming 90% in the 84.3% of the data sets. However, the difference is not statistically significant with the second best, the SVM with Gaussian kernel implemented in C using LibSVM, which achieves 92.3% of the maximum accuracy. A few models are clearly better than the remaining ones: random forest, SVM with Gaussian and polynomial kernels, extreme learning machine with Gaussian kernel, C5.0 and avNNet (a committee of multi-layer perceptrons implemented in R with the caret package). The random forest is clearly the best family of classifiers (3 out of 5 bests classi ers are RF), followed by SVM (4 classifiers in the top-10), neural networks and boosting ensembles (5 and 3 members in the top-20, respectively). © 2014 Manuel Fernández-Delgado, Eva Cernadas, Senén Barro and Dinani Amorim.
Dataset shift is a very common issue wherein the input data distribution shifts over time in non-stationary environments. A broad range of real-world systems face the challenge of dataset shift. In such systems, continuous monitoring of the process behavior and tracking the state of shift are required in order to decide about initiating adaptive corrections in a timely manner. This paper presents novel methods for covariate shift-detection tests based on a two-stage structure for both univariate and multivariate time-series. The first stage works in an online mode and it uses an exponentially weighted moving average (EWMA) model based control chart to detect the covariate shift-point in non-stationary time-series. The second stage validates the shift-detected by first stage using the Kolmogorov–Smirnov statistical hypothesis test (K–S test) in the case of univariate time-series and the Hotelling T-Squared multivariate statistical hypothesis test in the case of multivariate time-series. Additionally, several orthogonal transformations and blind source separation algorithms are investigated to counteract the adverse effect of cross-correlation in multivariate time-series on shift-detection performance. The proposed methods are suitable to be run in real-time. Their performance is evaluated through experiments using several synthetic and real-world datasets. Results show that all the covariate shifts are detected with much reduced false-alarms compared to other methods.
In time series prediction, one does often not know the properties of the underlying system generating the time series. For example, is it a closed system that is generating the time series or are there any external factors influencing the system? As a result of this, you often do not know beforehand whether a time series is stationary or nonstationary, and in the ideal case you do not want to make any assumptions about this. Therefore, if one wants to do time series prediction on such a system it would be nice if a model exists that is able to perform well on both nonstationary and stationary time series, and that the model adapts itself to the environment in which it is applied. In this thesis, we will experimentally investigate a method that hopefully has this property. We will look at the application of adaptive ensemble models of Extreme Learning Machines (ELMs) to the problem of one-step ahead prediction in (non)stationary time series.
Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.
Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient-based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O(1). Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Short-term load prediction, which forecasts a building's thermal load with a lead time ranging from seconds to a few days, is essential for not only monitoring and controlling the system operation, but also on-line scheduling. Dynamic cooling load forecasting, which belongs to short-term load prediction, is both meaningful for monitoring the system or fuzzy on-line scheduling and crucial for solving the time-lag problem to meet the heating, ventilation and air-conditioning system's time-varying cooling loads. Numerous studies have been carried out to develop dynamic load-forecasting models, and great achievements have been made. However, limitations in their applicability persist because most previous models are calendar- and time-based data-driven models that may fail when unexpected issues occur or special schedules are adopted. What's more, the inputs that were selected passively from the source data pools at hand rather than via active exploration may be insufficient and impair the accuracy of forecasting models. This paper proposes a novel dynamic forecasting model for building cooling loads that combines an artificial neural network with an ensemble approach. Based on physical principles other than the available data source, the inputs are explored actively and are independent from both calendar and time indicators, which make the forecasting model being capable of dealing with irregular occasions and unexpected schedules with high accuracy. A benchmark is proposed that uses the current load Q(t) as a forecasted cooling load Q^(t+i) and gives the minimum accuracy requirement for a dynamic forecasting model. The benchmark not only can be used to evaluate dynamic forecasting models that are validated by various case studies, but also ensures that the proposed forecasting model can be applied immediately to heating, ventilation and air-conditioning systems to tackle the time-lag problem.
This paper focuses on designing an adaptive radial basis function neural network U+0028 RBFNN U+0029 control method for a class of nonlinear systems with unknown parameters and bounded disturbances. The problems raised by the unknown functions and external disturbances in the nonlinear system are overcome by RBFNN, combined with the single parameter direct adaptive control method. The novel adaptive control method is designed to reduce the amount of computations effectively. The uniform ultimate boundedness of the closed U+002D loop system is guaranteed by the proposed controller. A coupled motor drives U+0028 CMD U+0029 system, which satisfies the structure of nonlinear system, is taken for simulation to confirm the effectiveness of the method. Simulations show that the developed adaptive controller has favorable performance on tracking desired signal and verify the stability of the closed-loop system.
In today's restructuring electricity market, short-term load forecasting (STLF) is an essential tool for the electricity utilities to predict future scenario and act towards a profitable policy. The electric load demand is highly influenced by the thermal inertia due to the climatic factors. These influential climatic factors are different in different regions. Therefore, it is necessary to have a region specific STLF model for load forecasting under regional climatic conditions. This paper proposes a regional hybrid STLF model utilizing SVM with a new technique, called grasshopper optimization algorithm (GOA), to evaluate its suitable parameters. This study is carried out in Assam, a state of India and proposed GOA-SVM model is targeted for forecasting the load under local climatic conditions. The proposed model uses the similar day approach (SDA) to satisfy the regional climatic requirements. The results of the proposed model show better accuracy comparing to results generated with classical STLF model of incorporating temperature universally as the only climatic factor. To further affirm the efficacy of the proposed model, same inputs are delivered in two alternative hybrid models, namely GA-SVM (GA with SVM) and PSO-SVM (PSO with SVM). The results indicate that the proposed model outperforms the other hybrid models.
Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.
The precious metal markets are subject to the influence of complicated factor characterized by the interrelationship and nonlinearity with the short burst of noise data components. In this paper we propose a new Multivariate Empirical Mode Decomposition (MEMD) denoising model to identify the noise factors in the multiscale domain and forecast the precious metal price movement. Since the MEMD model is introduced to analyze and project the inter-relationship between different precious metal prices in the multiscale domain, the transient noise factor is identified, analyzed and suppressed. The movement of the reconstructed precious metal price is modeled using the ARMA model with higher accuracy. Empirical studies using the typical precious metal price data show that the proposed model achieves the statistically significant forecasting performance improvement, which provides the ex-post evidence on the noise factors identified. Further comparative studies of both MEMD and wavelet analysis based models show the complimentary relationship between these two popular multi scale models. We also found that Gold and Silver markets are subject to the similar influence of disruptive noises while Palladium and Platinum markets are subject to the influence of other influencing factors. The disruptive influencing factor is expected to be Euro/Dollar exchange rate.
Reliable wind speed forecasting is essential for wind power integration in wind power generation system. The purpose of paper is to develop a novel hybrid model for short-term wind speed forecasting and demonstrates its efficiency. In the proposed model, a compound structure of extreme learning machine (ELM) based on feature selection and parameter optimization using hybrid backtracking search algorithm (HBSA) is employed as the predictor. The real-valued BSA (RBSA) is exploited to search for the optimal combination of weights and bias of ELM while the binary-valued BSA (BBSA) is exploited as a feature selection method applying on the candidate inputs predefined by partial autocorrelation function (PACF) values to reconstruct the input-matrix. Due to the volatility and randomness of wind speed signal, an optimized variational mode decomposition (OVMD) is employed to eliminate the redundant noises. The parameters of the proposed OVMD are determined according to the center frequencies of the decomposed modes and the residual evaluation index (REI). The wind speed signal is decomposed into a few modes via OVMD. The aggregation of the forecasting results of these modes constructs the final forecasting result of the proposed model. The proposed hybrid model has been applied on the mean half-hour wind speed observation data from two wind farms in Inner Mongolia, China and 10-min wind speed data from the Sotavento Galicia wind farm are studied as an additional case. Parallel experiments have been designed to compare with the proposed model. Results obtained from this study indicate that the proposed hybrid model can provide an effective modeling approach to capture the nonlinear characteristics of wind speed signal and thus providing more satisfactory forecasting results.
In this paper, a hybrid learning approach, which combines the extreme learning machine (ELM) with a new switching delayed PSO (SDPSO) algorithm, is proposed for the problem of the short-term load forecasting (STLF). In particular, the input weights and biases of ELM are optimized by a new developed SDPSO algorithm, where the delayed information of locally best particle and globally best particle are exploited to update the velocity of particle. By testing the proposed SDPSO-ELM in a comprehensive manner on a tanh function, this approach obtain better generalization performance and can also avoid adding unnecessary hidden nodes and overtraining problems. Moreover, it has shown outstanding performance than other state-of-the-art ELMs. Finally, the proposed SDPSO-ELM algorithm is successfully applied to the STLF of power system. Experiment results demonstrate that the proposed learning algorithm can get better forecasting results in comparison with the radial basis function neural network (RBFNN) algorithm.
This paper proposes a generalized exponential moving average (EMA) model, a new stochastic volatility model with time-varying expected return in financial markets. In particular, we effectively apply a particle filter (PF) to sequential estimation of states and parameters in a state space framework. Moreover, we develop three types of anomaly detectors, which are implemented easily in the PF algorithm to be used for investment decision. As a result, a simple investment strategy with our scheme is superior to the one based on the standard EMA and well-known traditional strategies such as equally-weighted, minimum-variance and risk parity portfolios.
Our dataset is monthly total returns of global financial assets such as stocks, bonds and REITs, and investment performances are evaluated with various statistics, namely compound returns, Sharpe ratios, Sortino ratios and drawdowns.
In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the Levenberg–Marquardt method is proposed to improve the learning accuracy of neural networks. Third, a feature selection method based on the conditional mutual information is developed to select a compact set of input variables for the forecasting model. Fourth, to realize an accurate ensemble forecast, partial least squares regression is utilized as a combining approach to aggregate the individual forecasts. Numerical testing shows that proposed method can obtain better forecasting results in comparison with other standard and state-of-the-art methods.
From the Publisher:From mobile communications to robotics to space technology to medical instrumentation, new technologies are demanding increasingly complex methods of digital signal processing (DSP). This book shows researchers how recurrent neural networks can be implemented to expand the range of traditional signal processing techniques. Featuring original research on stability in neural networks, the book combines rigorous mathematical analysis with application examples. Experimental evidence as well as an overview of existing approaches are also included. Market: Engineers working in signal processing, neural networks, communications, nonlinear control, and time series analysis.
Accurate forecasting of electric load has always been the most important issues in the electricity industry, particularly for developing countries. Due to the various influences, electric load forecasting reveals highly nonlinear characteristics. Recently, support vector regression (SVR), with nonlinear mapping capabilities of forecasting, has been successfully employed to solve nonlinear regression and time series problems. However, it is still lack of systematic approaches to determine appropriate parameter combination for a SVR model. This investigation elucidates the feasibility of applying chaotic particle swarm optimization (CPSO) algorithm to choose the suitable parameter combination for a SVR model. The empirical results reveal that the proposed model outperforms the other two models applying other algorithms, genetic algorithm (GA) and simulated annealing algorithm (SA). Finally, it also provides the theoretical exploration of the electric load forecasting support system (ELFSS).
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.
Le-Mehkar U-Fituah Haifa Israel, I.M. ha-energyah veha Tashtit, 1981, Energy Consumption and Economic Growth inIsrael: Trend Analysis
- J Bargur
- A Mandel
- M Ha-Tekhniyon
Le-Mehkar U-Fituah Haifa Israel, I.M. ha-energyah veha Tashtit, 1981, Energy Consumption and Economic Growth inIsrael: Trend Analysis
- bargur
Blending Big Data Analytics: Review on Challenges and a Recent Study
- narudin