Research

20 pages, 404 KiB

Open AccessArticle

Short-Term Power Forecasting Model for Photovoltaic Plants Based on Historical Similarity

by

Claudio Monteiro

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Tiago Santos

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L. Alfredo Fernandez-Jimenez

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Ignacio J. Ramirez-Rosado

and

M. Sonia Terreros-Olarte

Energies 2013, 6(5), 2624-2643; https://doi.org/10.3390/en6052624 - 22 May 2013

Cited by 91 | Viewed by 8641

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)

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19 pages, 16348 KiB

Open AccessArticle

A New Two-Stage Approach to Short Term Electrical Load Forecasting

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Energies 2013, 6(4), 2130-2148; https://doi.org/10.3390/en6042130 - 18 Apr 2013

Cited by 9 | Viewed by 5600

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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20 pages, 357 KiB

Open AccessArticle

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

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Energies 2013, 6(4), 2110-2129; https://doi.org/10.3390/en6042110 - 17 Apr 2013

Cited by 16 | Viewed by 5316

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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12 pages, 1459 KiB

Open AccessArticle

Hybrid Predictive Models for Accurate Forecasting in PV Systems

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Energies 2013, 6(4), 1918-1929; https://doi.org/10.3390/en6041918 - 03 Apr 2013

Cited by 82 | Viewed by 7337

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)

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15 pages, 542 KiB

Open AccessArticle

Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting

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Energies 2013, 6(4), 1887-1901; https://doi.org/10.3390/en6041887 - 02 Apr 2013

Cited by 110 | Viewed by 8524

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)

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24 pages, 1090 KiB

Open AccessArticle

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

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, Antonio J. Sanchez-Esguevillas and

Jaime Lloret

Energies 2013, 6(3), 1385-1408; https://doi.org/10.3390/en6031385 - 05 Mar 2013

Cited by 120 | Viewed by 9152

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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15 pages, 2527 KiB

Open AccessArticle

Load Forecast Model Switching Scheme for Improved Robustnessto Changes in Building Energy Consumption Patterns

by

Jaeyeong Yoo

and

Kyeon Hur

Energies 2013, 6(3), 1329-1343; https://doi.org/10.3390/en6031329 - 05 Mar 2013

Cited by 7 | Viewed by 5024

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)

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15 pages, 334 KiB

Open AccessArticle

A Bayesian Method for Short-Term Probabilistic Forecasting of Photovoltaic Generation in Smart Grid Operation and Control

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Energies 2013, 6(2), 733-747; https://doi.org/10.3390/en6020733 - 06 Feb 2013

Cited by 108 | Viewed by 8969

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)

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34 pages, 1761 KiB

Open AccessArticle

Quantile Forecasting of Wind Power Using Variability Indices

by

Georgios Anastasiades

and

Patrick McSharry

Energies 2013, 6(2), 662-695; https://doi.org/10.3390/en6020662 - 05 Feb 2013

Cited by 26 | Viewed by 6622

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)

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19 pages, 3302 KiB

Open AccessArticle

by

Félix Iglesias

and

Wolfgang Kastner

Energies 2013, 6(2), 579-597; https://doi.org/10.3390/en6020579 - 24 Jan 2013

Cited by 171 | Viewed by 13964

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)

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15 pages, 708 KiB

Open AccessArticle

Day-Ahead Electricity Price Forecasting Using a Hybrid Principal Component Analysis Network

by

Ying-Yi Hong

and

Ching-Ping Wu

Energies 2012, 5(11), 4711-4725; https://doi.org/10.3390/en5114711 - 19 Nov 2012

Cited by 40 | Viewed by 6926

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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16 pages, 361 KiB

Open AccessArticle

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

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and

Energies 2012, 5(11), 4430-4445; https://doi.org/10.3390/en5114430 - 08 Nov 2012

Cited by 111 | Viewed by 8721

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)

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19 pages, 628 KiB

Open AccessArticle

An Improved Quantum-Behaved Particle Swarm Optimization Method for Economic Dispatch Problems with Multiple Fuel Options and Valve-Points Effects

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Energies 2012, 5(9), 3655-3673; https://doi.org/10.3390/en5093655 - 19 Sep 2012

Cited by 30 | Viewed by 6024

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)

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18 pages, 334 KiB

Open AccessArticle

A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power

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and Xuebin Zhang

Energies 2012, 5(9), 3329-3346; https://doi.org/10.3390/en5093329 - 05 Sep 2012

Cited by 42 | Viewed by 6404

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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