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Special Issue "Hybrid Advanced Techniques for Forecasting in Energy Sector"

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A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (31 December 2012)

Special Issue Editor

Guest Editor
Prof. Dr. Wei-Chiang Hong

School of Economics & Management, Nanjing Tech University, Nanjing, 211800, China
Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
Website | E-Mail
Interests: computational intelligence (neural networks; evolutionary computation); application of forecasting technology (ARIMA; support vector regression; chaos theory)

Special Issue Information

Dear Colleagues,

The present issue Hybrid Advanced Techniques for Forecasting in Energy Sector focuses on load/price/wind speed forecasting, which are the prime factors in modern restructured power market by any novel hybrid advanced techniques to provide significant forecasting accuracy improvements (i.e., proved by statistical test). Hybrid advanced models of this issue is not only concentrated on hybrid evolutionary algorithms or hybrid chaos theory, fuzzy theory, cloud theory with evolutionary algorithms to determine suitable parameters for an existed model, but also on hybridization of two or above existed models, such as neuro-fuzzy model, BPNN-fuzzy model, and so on.

Papers are sought on recent novel ideas by hybridizing or combining intelligent computation technologies in all fields forecasting in energy sector: genetic algorithm, simulated annealing algorithm, particle swarm optimization, ant colony optimization, immune algorithm, artificial bee colony algorithm, chaotic mapping sequence (including Logistic mapping, Cat mapping, Tent mapping, and An mapping, etc.), cloud theory, fuzzy theory, artificial neural networks, recurrent mechanism, feed forward mechanism, back-propagation mechanism, seasonal mechanism, etc..

Manuscripts on power transmission design/prediction or IC treatments of economic dispatch scheduling are not targeted in this edition and should be submitted elsewhere.

Dr. Wei-Chiang Hong
Guest Editor

Keywords

  • hybrid models, combined models
  • energy load forecasting
  • hybrid evolutionary algorithms (genetic algorithm, simulated annealing algorithm, particle swarm optimization, ant colony optimization, immune algorithm, artificial bee colony algorithm, fire fly algorithm, harmony search)
  • chaotic mapping sequence (Logistic mapping, Cat mapping, Tent mapping, and An mapping)
  • cloud theory
  • fuzzy theory
  • artificial neural networks (recurrent mechanism, feed forward mechanism, back-propagation mechanism)
  • seasonal mechanism
  • wavelet transform

Published Papers (14 papers)

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Research

Open AccessArticle Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity
Energies 2013, 6(5), 2624-2643; doi:10.3390/en6052624
Received: 28 December 2012 / Revised: 15 May 2013 / Accepted: 15 May 2013 / Published: 22 May 2013
Cited by 11 | PDF Full-text (404 KB) | HTML Full-text | XML Full-text
Abstract
This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied
[...] Read more.
This paper proposes a new model for short-term forecasting of electric energy production in a photovoltaic (PV) plant. The model is called HIstorical SImilar MIning (HISIMI) model; its final structure is optimized by using a genetic algorithm, based on data mining techniques applied to historical cases composed by past forecasted values of weather variables, obtained from numerical tools for weather prediction, and by past production of electric power in a PV plant. The HISIMI model is able to supply spot values of power forecasts, and also the uncertainty, or probabilities, associated with those spot values, providing new useful information to users with respect to traditional forecasting models for PV plants. Such probabilities enable analysis and evaluation of risk associated with those spot forecasts, for example, in offers of energy sale for electricity markets. The results of spot forecasting of an illustrative example obtained with the HISIMI model for a real-life grid-connected PV plant, which shows high intra-hour variability of its actual power output, with forecasting horizons covering the following day, have improved those obtained with other two power spot forecasting models, which are a persistence model and an artificial neural network model. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle A New Two-Stage Approach to Short Term Electrical Load Forecasting
Energies 2013, 6(4), 2130-2148; doi:10.3390/en6042130
Received: 28 December 2012 / Revised: 11 March 2013 / Accepted: 1 April 2013 / Published: 18 April 2013
Cited by 3 | PDF Full-text (16348 KB) | HTML Full-text | XML Full-text
Abstract
In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model.
[...] Read more.
In the deregulated energy market, the accuracy of load forecasting has a significant effect on the planning and operational decision making of utility companies. Electric load is a random non-stationary process influenced by a number of factors which make it difficult to model. To achieve better forecasting accuracy, a wide variety of models have been proposed. These models are based on different mathematical methods and offer different features. This paper presents a new two-stage approach for short-term electrical load forecasting based on least-squares support vector machines. With the aim of improving forecasting accuracy, one more feature was added to the model feature set, the next day average load demand. As this feature is unknown for one day ahead, in the first stage, forecasting of the next day average load demand is done and then used in the model in the second stage for next day hourly load forecasting. The effectiveness of the presented model is shown on the real data of the ISO New England electricity market. The obtained results confirm the validity advantage of the proposed approach. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Open AccessArticle Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings
Energies 2013, 6(4), 2110-2129; doi:10.3390/en6042110
Received: 18 January 2013 / Revised: 12 March 2013 / Accepted: 18 March 2013 / Published: 17 April 2013
Cited by 5 | PDF Full-text (357 KB) | HTML Full-text | XML Full-text
Abstract
Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption
[...] Read more.
Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Open AccessArticle Hybrid Predictive Models for Accurate Forecasting in PV Systems
Energies 2013, 6(4), 1918-1929; doi:10.3390/en6041918
Received: 9 January 2013 / Revised: 8 February 2013 / Accepted: 26 February 2013 / Published: 3 April 2013
Cited by 28 | PDF Full-text (1459 KB) | HTML Full-text | XML Full-text
Abstract
The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of
[...] Read more.
The accurate forecasting of energy production from renewable sources represents an important topic also looking at different national authorities that are starting to stimulate a greater responsibility towards plants using non-programmable renewables. In this paper the authors use advanced hybrid evolutionary techniques of computational intelligence applied to photovoltaic systems forecasting, analyzing the predictions obtained by comparing different definitions of the forecasting error. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting
Energies 2013, 6(4), 1887-1901; doi:10.3390/en6041887
Received: 28 November 2012 / Revised: 2 February 2013 / Accepted: 25 March 2013 / Published: 2 April 2013
Cited by 12 | PDF Full-text (542 KB) | HTML Full-text | XML Full-text
Abstract
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a
[...] Read more.
Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Load Forecast Model Switching Scheme for Improved Robustnessto Changes in Building Energy Consumption Patterns
Energies 2013, 6(3), 1329-1343; doi:10.3390/en6031329
Received: 4 January 2013 / Revised: 27 January 2013 / Accepted: 26 February 2013 / Published: 5 March 2013
Cited by 5 | PDF Full-text (2527 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a new, accurate load forecasting technique robust to fluctuations due to unusual load behavioral changes in buildings, i.e., the potential for small commercial buildings with heterogeneous stores. The proposed scheme is featured with two functional components: data classification by daily
[...] Read more.
This paper presents a new, accurate load forecasting technique robust to fluctuations due to unusual load behavioral changes in buildings, i.e., the potential for small commercial buildings with heterogeneous stores. The proposed scheme is featured with two functional components: data classification by daily characteristics and automatic forecast model switching. The scheme extracts daily characteristics of the input load data and arranges the load data into weekday and weekend data. Forecasting is conducted based on a selected model among ARMAX (autoregressive moving average with exogenous variable) models with the processed input data. Kalman filtering is applied to estimate model parameters. The model-switching scheme monitors the accumulated error and substitutes a backup load model for the currently working model, when the accumulated error exceeds a threshold value, to reduce the increased bias error due to the change in the consumption pattern. This switching reinforces the limited performance of parameter estimation given a fixed structure and, thus, forecasting capability. The study results demonstrate that the proposed scheme is reasonably accurate and even robust to changes in the electricity use patterns. It should help improve the performance for building control systems for energy efficiency. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks
Energies 2013, 6(3), 1385-1408; doi:10.3390/en6031385
Received: 28 November 2012 / Revised: 18 February 2013 / Accepted: 20 February 2013 / Published: 5 March 2013
Cited by 23 | PDF Full-text (1090 KB) | HTML Full-text | XML Full-text
Abstract
Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to
[...] Read more.
Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Open AccessArticle A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control
Energies 2013, 6(2), 733-747; doi:10.3390/en6020733
Received: 28 December 2012 / Revised: 24 January 2013 / Accepted: 25 January 2013 / Published: 6 February 2013
Cited by 28 | PDF Full-text (334 KB) | HTML Full-text | XML Full-text
Abstract
A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive
[...] Read more.
A new short-term probabilistic forecasting method is proposed to predict the probability density function of the hourly active power generated by a photovoltaic system. Firstly, the probability density function of the hourly clearness index is forecasted making use of a Bayesian auto regressive time series model; the model takes into account the dependence of the solar radiation on some meteorological variables, such as the cloud cover and humidity. Then, a Monte Carlo simulation procedure is used to evaluate the predictive probability density function of the hourly active power by applying the photovoltaic system model to the random sampling of the clearness index distribution. A numerical application demonstrates the effectiveness and advantages of the proposed forecasting method. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Quantile Forecasting of Wind Power Using Variability Indices
Energies 2013, 6(2), 662-695; doi:10.3390/en6020662
Received: 23 November 2012 / Revised: 12 January 2013 / Accepted: 22 January 2013 / Published: 5 February 2013
Cited by 11 | PDF Full-text (1761 KB) | HTML Full-text | XML Full-text
Abstract
Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp
[...] Read more.
Wind power forecasting techniques have received substantial attention recently due to the increasing penetration of wind energy in national power systems. While the initial focus has been on point forecasts, the need to quantify forecast uncertainty and communicate the risk of extreme ramp events has led to an interest in producing probabilistic forecasts. Using four years of wind power data from three wind farms in Denmark, we develop quantile regression models to generate short-term probabilistic forecasts from 15 min up to six hours ahead. More specifically, we investigate the potential of using various variability indices as explanatory variables in order to include the influence of changing weather regimes. These indices are extracted from the same wind power series and optimized specifically for each quantile. The forecasting performance of this approach is compared with that of appropriate benchmark models. Our results demonstrate that variability indices can increase the overall skill of the forecasts and that the level of improvement depends on the specific quantile. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Analysis of Similarity Measures in Times Series Clustering for the Discovery of Building Energy Patterns
Energies 2013, 6(2), 579-597; doi:10.3390/en6020579
Received: 22 November 2012 / Revised: 31 December 2012 / Accepted: 11 January 2013 / Published: 24 January 2013
Cited by 18 | PDF Full-text (3302 KB) | HTML Full-text | XML Full-text
Abstract
Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment
[...] Read more.
Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment and parameterization of the whole clustering task is complex and submitted to several uncertainties, being the similarity metric one of the first decisions to be made in order to establish how the distance between two independent vectors must be measured. The present paper checks the effect of similarity measures in the application of clustering for discovering representatives in cases where correlation is supposed to be an important factor to consider, e.g., time series. This is a necessary step for the optimized design and development of efficient clustering-based models, predictors and controllers of time-dependent processes, e.g., building energy consumption patterns. In addition, clustered-vector balance is proposed as a validation technique to compare clustering performances. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network
Energies 2012, 5(11), 4711-4725; doi:10.3390/en5114711
Received: 5 September 2012 / Accepted: 12 November 2012 / Published: 19 November 2012
Cited by 19 | PDF Full-text (708 KB) | HTML Full-text | XML Full-text
Abstract
Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market
[...] Read more.
Bidding competition is one of the main transaction approaches in a deregulated electricity market. Locational marginal prices (LMPs) resulting from bidding competition and system operation conditions indicate electricity values at a node or in an area. The LMP reveals important information for market participants in developing their bidding strategies. Moreover, LMP is also a vital indicator for the Security Coordinator to perform market redispatch for congestion management. This paper presents a method using a principal component analysis (PCA) network cascaded with a multi-layer feedforward (MLF) network for forecasting LMPs in a day-ahead market. The PCA network extracts essential features from periodic information in the market. These features serve as inputs to the MLF network for forecasting LMPs. The historical LMPs in the PJM market are employed to test the proposed method. It is found that the proposed method is capable of forecasting day-ahead LMP values efficiently. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
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Open AccessArticle Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm
Energies 2012, 5(11), 4430-4445; doi:10.3390/en5114430
Received: 14 September 2012 / Revised: 18 October 2012 / Accepted: 2 November 2012 / Published: 8 November 2012
Cited by 39 | PDF Full-text (361 KB) | HTML Full-text | XML Full-text
Abstract
The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate
[...] Read more.
The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects
Energies 2012, 5(9), 3655-3673; doi:10.3390/en5093655
Received: 2 July 2012 / Revised: 10 August 2012 / Accepted: 22 August 2012 / Published: 19 September 2012
Cited by 11 | PDF Full-text (628 KB) | HTML Full-text | XML Full-text
Abstract
Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective
[...] Read more.
Quantum-behaved particle swarm optimization (QPSO) is an efficient and powerful population-based optimization technique, which is inspired by the conventional particle swarm optimization (PSO) and quantum mechanics theories. In this paper, an improved QPSO named SQPSO is proposed, which combines QPSO with a selective probability operator to solve the economic dispatch (ED) problems with valve-point effects and multiple fuel options. To show the performance of the proposed SQPSO, it is tested on five standard benchmark functions and two ED benchmark problems, including a 40-unit ED problem with valve-point effects and a 10-unit ED problem with multiple fuel options. The results are compared with differential evolution (DE), particle swarm optimization (PSO) and basic QPSO, as well as a number of other methods reported in the literature in terms of solution quality, convergence speed and robustness. The simulation results confirm that the proposed SQPSO is effective and reliable for both function optimization and ED problems. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)
Open AccessArticle A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power
Energies 2012, 5(9), 3329-3346; doi:10.3390/en5093329
Received: 20 April 2012 / Revised: 15 August 2012 / Accepted: 21 August 2012 / Published: 5 September 2012
Cited by 19 | PDF Full-text (334 KB) | HTML Full-text | XML Full-text
Abstract
Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups
[...] Read more.
Many models have been developed to forecast wind farm power output. It is generally difficult to determine whether the performance of one model is consistently better than that of another model under all circumstances. Motivated by this finding, we aimed to integrate groups of models into an aggregated model using fuzzy theory to obtain further performance improvements. First, three groups of least squares support vector machine (LS-SVM) forecasting models were developed: univariate LS-SVM models, hybrid models using auto-regressive moving average (ARIMA) and LS-SVM and multivariate LS-SVM models. Each group of models is selected by a decorrelation maximisation method, and the remaining models can be regarded as experts in forecasting. Next, fuzzy aggregation and a defuzzification procedure are used to combine all of these forecasting results into the final forecast. For sample randomization, we statistically compare models. Results show that this group-forecasting model performs well in terms of accuracy and consistency. Full article
(This article belongs to the Special Issue Hybrid Advanced Techniques for Forecasting in Energy Sector)

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