Special Issue "Intelligent Energy Demand Forecasting"
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A special issue of Energies (ISSN 1996-1073).
Deadline for manuscript submissions: closed (31 December 2011)
Special Issue Editors
Guest Editor
Dr. Wei-Chiang Hong
Department of Information Management, Oriental Institute of Technology, No. 58, Sec. 2, Sichuan Rd., Panchiao, Taipei, 220, Taiwan
E-Mail: samuelsonhong@gmail.com
Guest Editor
Dr. Yucheng Dong
Department of Organization and Management, School of Management, Xi’an Jiaotong University, China
E-Mail: ycdong@mail.xjtu.edu.cn
Special Issue Information
Dear Colleagues,
The present issue “Intelligent Energy Demand Forecasting” focuses on accurate energy demand modeling by intelligent computation (IC) approaches to provide well energy planning, accurate energy expenditure prediction, and energy distributing efficiency. Particular forecasting technologies of this issue is concentrated on evolutionary computing, neural computing, fuzzy computing, natural computing, probabilistic computing, wavelet transform, and chaotic sequence with evolutionary algorithms, etc.. Papers are sought on recent novel IC technology developments with major application areas in (but not limited to): short term load forecasting (STLF), long term load forecasting, wind energy demand forecasting, solar energy demand forecasting, novel energy (Green energy, ocean energy, etc.) demand forecasting, and business energy demand patterns forecasting. 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,
Dr. Yucheng Dong
Guest Editors
Submission
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. Papers will be published continuously (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are refereed through a peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed Open Access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs).
Keywords
- short term load forecasting (STLF)
- energy demand forecasting
- intelligent computation
- evolutionary computing
- neural computing
- fuzzy computing
- natural computing
- probabilistic computing
- wavelet transform
- chaotic sequence
- evolutionary algorithms
Published Papers (7 papers)
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Received: 4 February 2011; in revised form: 22 February 2011 / Accepted: 9 March 2011 / Published: 10 March 2011
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Abstract: Short-term load forecast (STLF) is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.
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Received: 24 March 2011; in revised form: 7 June 2011 / Accepted: 14 June 2011 / Published: 17 June 2011
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Abstract: Accurate electric load forecasting has become the most important issue in energy management; however, electric load demonstrates a seasonal/cyclic tendency from economic activities or the cyclic nature of climate. The applications of the support vector regression (SVR) model to deal with seasonal/cyclic electric load forecasting have not been widely explored. The purpose of this paper is to present a SVR model which combines the seasonal adjustment mechanism and a chaotic immune algorithm (namely SSVRCIA) to forecast monthly electric loads. Based on the operation procedure of the immune algorithm (IA), if the population diversity of an initial population cannot be maintained under selective pressure, then IA could only seek for the solutions in the narrow space and the solution is far from the global optimum (premature convergence). The proposed chaotic immune algorithm (CIA) based on the chaos optimization algorithm and IA, which diversifies the initial definition domain in stochastic optimization procedures, is used to overcome the premature local optimum issue in determining three parameters of a SVR model. A numerical example from an existing reference is used to elucidate the forecasting performance of the proposed SSVRCIA model. The forecasting results indicate that the proposed model yields more accurate forecasting results than the ARIMA and TF-ε-SVR-SA models, and therefore the SSVRCIA model is a promising alternative for electric load forecasting.
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Received: 3 May 2011; in revised form: 27 July 2011 / Accepted: 9 August 2011 / Published: 22 August 2011
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Abstract: Demand planning for electricity consumption is a key success factor for the development of any countries. However, this can only be achieved if the demand is forecasted accurately. In this research, different forecasting methods—autoregressive integrated moving average (ARIMA), artificial neural network (ANN) and multiple linear regression (MLR)—were utilized to formulate prediction models of the electricity demand in Thailand. The objective was to compare the performance of these three approaches and the empirical data used in this study was the historical data regarding the electricity demand (population, gross domestic product: GDP, stock index, revenue from exporting industrial products and electricity consumption) in Thailand from 1986 to 2010. The results showed that the ANN model reduced the mean absolute percentage error (MAPE) to 0.996%, while those of ARIMA and MLR were 2.80981 and 3.2604527%, respectively. Based on these error measures, the results indicated that the ANN approach outperformed the ARIMA and MLR methods in this scenario. However, the paired test indicated that there was no significant difference among these methods at α = 0.05. According to the principle of parsimony, the ARIMA and MLR models might be preferable to the ANN one because of their simple structure and competitive performance
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Received: 22 July 2011; in revised form: 14 September 2011 / Accepted: 20 September 2011 / Published: 28 September 2011
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Abstract: Monthly forecasting of electric energy consumption is important for planning the generation and distribution of power utilities. However, the features of this time series are so complex that directly modeling is difficult. Three kinds of relatively simple series can be derived when a discrete wavelet transform is used to extract the raw features, namely, the rising trend, periodic waves, and stochastic series. After the elimination of the stochastic series, the rising trend and periodic waves were modeled separately by a grey model and radio basis function neural networks. Adding the forecasting values of each model can yield the forecasting results for monthly electricity consumption. The grey model has a good capability for simulating any smoothing convex trend. In addition, this model can mitigate minor stochastic effects on the rising trend. The extracted periodic wave series, which contain relatively less information and comprise simple regular waves, can improve the generalization capability of neural networks. The case study on electric energy consumption in China shows that the proposed method is better than those traditionally used in terms of both forecasting precision and expected risk.
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Received: 29 September 2011; in revised form: 8 November 2011 / Accepted: 7 December 2011 / Published: 22 December 2011
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Abstract: This paper proposes a structure for long-term energy demand forecasting. The proposed hybrid approach, called HPLLNF, uses the local linear neuro-fuzzy (LLNF) model as the forecaster and utilizes the Hodrick–Prescott (HP) filter for extraction of the trend and cyclic components of the energy demand series. Besides, the sophisticated technique of mutual information (MI) is employed to select the most relevant input features with least possible redundancies for the forecast model. Each generated component by the HP filter is then modeled through an LLNF model. Starting from an optimal least square estimation, the local linear model tree (LOLIMOT) learning algorithm increases the complexity of the LLNF model as long as its performance is improved. The proposed HPLLNF model with MI-based input selection is applied to the problem of long-term energy forecasting in three different case studies, including forecasting of the gasoline, crude oil and natural gas demand over the next 12 months. The obtained forecasting results reveal the noteworthy performance of the proposed approach for long-term energy demand forecasting applications.
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Received: 15 December 2011; in revised form: 16 January 2012 / Accepted: 16 January 2012 / Published: 19 January 2012
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Abstract: Due to the very complex sets of component systems, interrelated thermodynamic processes and seasonal change in operating conditions, it is relatively difficult to find an accurate model for turbine cycle of nuclear power plants (NPPs). This paper deals with the modeling of turbine cycles to predict turbine-generator output using an adaptive neuro-fuzzy inference system (ANFIS) for Unit 1 of the Kuosheng NPP in Taiwan. Plant operation data obtained from Kuosheng NPP between 2006 and 2011 were verified using a linear regression model with a 95% confidence interval. The key parameters of turbine cycle, including turbine throttle pressure, condenser backpressure, feedwater flow rate and final feedwater temperature are selected as inputs for the ANFIS based turbine cycle model. In addition, a thermodynamic turbine cycle model was developed using the commercial software PEPSE® to compare the performance of the ANFIS based turbine cycle model. The results show that the proposed ANFIS based turbine cycle model is capable of accurately estimating turbine-generator output and providing more reliable results than the PEPSE® based turbine cycle models. Moreover, test results show that the ANFIS performed better than the artificial neural network (ANN), which has also being tried to model the turbine cycle. The effectiveness of the proposed neuro-fuzzy based turbine cycle model was demonstrated using the actual operating data of Kuosheng NPP. Furthermore, the results also provide an alternative approach to evaluate the thermal performance of nuclear power plants.
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Received: 29 January 2012; in revised form: 18 February 2012 / Accepted: 23 February 2012 / Published: 1 March 2012
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Abstract: In this paper, petroleum product (mainly petrol and diesel) consumption in the transportation sector of China is analyzed. This was based on the Bayesian linear regression theory and Markov Chain Monte Carlo method (MCMC), establishing a demand-forecast model of petrol and diesel consumption introduced into the analytical framework with explanatory variables of urbanization level, per capita GDP, turnover of passengers (freight) in aggregate (TPA, TFA), and civilian vehicle number (CVN) and explained variables of petrol and diesel consumption. Furthermore, we forecast the future consumer demand for oil products during “The 12th Five Year Plan” (2011–2015) based on the historical data covering from 1985 to 2009, finding that urbanization is the most sensitive factor, with a strong marginal effect on petrol and diesel consumption in this sector. From the viewpoint of prediction interval value, urbanization expresses the lower limit of the predicted results, and CVN the upper limit of the predicted results. Predicted value from other independent variables is in the range of predicted values which display a validation range and reference standard being much more credible for policy makers. Finally, a comparison between the predicted results from autoregressive integrated moving average models (ARIMA) and others is made to assess our task.
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Last update: 27 December 2012
