Confidence intervals for neural network based short-term loadforecasting

Using traditional statistical models, like ARMA and multilinear regression, confidence intervals can be computed for the short-term electric load forecasting, assuming that the forecast errors are independent and Gaussian distributed. In this paper, the 1 to 24 steps ahead load forecasts are obtained through multilayer perceptrons trained by the backpropagation algorithm. Three techniques for the computation of confidence intervals for this neural network based short-term load forecasting are presented: (1) error output; (2) resampling; and (3) multilinear regression adapted to neural networks. A comparison of the three techniques is performed through simulations of online forecasting     

New models for integrated short-term forward electricity markets

In a typical short-term forward wholesale electricity market where products are auctioned sequentially, one often observes significant market inefficiency and price volatility-thus the growing impetus in developing integrated short-term forward markets where electric energy, reserves, and transmission capacity are auctioned simultaneously. Such markets need new computational methods and models for determining market clearing prices and physical (delivery/consumption) schedules. The purpose of this paper is to examine key aspects of current modeling and pricing methods in short-term forward wholesale electricity markets and to introduce new models suitable for clearing price-based markets of integrated trades of energy, reserve, and transmission. Specifically, an analysis of the impacts of various pricing rules and bidding requirements on market operations is presented, the selection of optimization objectives is discussed, and a new model of transmission congestion and multiproducts simultaneous auction is introduced. Examples are used where appropriate.     

An adaptive neural network approach to one-week ahead loadforecasting

A neural network approach is proposed for one-week ahead load forecasting. This approach uses a linear adaptive neuron or adaptive linear combiner called Adaline. An energy spectrum is used to analyze the periodic components in a load sequence. The load sequence mainly consists of three components: base load component, and low and high frequency load components. Each load component has a unique frequency range. A load decomposition is made for the load sequence using digital filters with different passband frequencies. After load decomposition, each load component can be forecasted by an Adaline. Each Adaline has an input sequence, an output sequence, and a desired response-signal sequence. It also has a set of adjustable parameters called the weight vector. In load forecasting, the weight vector is designed to make the output sequence, the forecasted load, follow the actual load sequence; it also has a minimized least mean square error. This approach is useful in forecasting unit scheduling commitments. Mean absolute percentage errors of less than 3.4% are presented from five months of utility data, thus demonstrating the high degree of accuracy that can be obtained without dependence on weather forecasts     

Some analytical results pertaining to Cournot models for short-term electricity markets

This paper provides some theoretical results pertaining to the Cournot model applied to short-term electricity markets. Price, quantities and profits are first obtained, and then results related to sensitivities and limit values are derived and discussed. The cases of both several identical Cournot producers and one dominant Cournot producer are analyzed. A case example illustrates the results obtained.     

一种新型短期电力负荷组合预测方法

短期负荷预测的准确性对电力系统的安全运行和生产安排起着至关重要的作用。针对单一负荷预测方法较难准确预测电力负荷的情况,提出了一种新颖的组合预测方法。该方法采用混沌时间序列和基于RBF-ARX模型的预测方法对日负荷进行加权组合预测,并通过与单个预测方法进行比较,证明了该组合预测方法精度更高,相对误差更小。     

An individual welfare maximization algorithm for electricity markets

An algorithm that allows a market participant to maximize its individual welfare in electricity spot markets is presented. The use of the algorithm in determining market equilibrium points, called Nash equilibria, is demonstrated. The start of the algorithm is a spot market model that uses the optimal power flow (OPF), with a full representation of the transmission system and inclusion of consumer bidding. The algorithm utilizes price and dispatch sensitivities, available from the Hessian matrix and gradient of the OPF, to help determine an optimal change in an individual's bid. The algorithm is shown to be successful in determining local welfare maxima, and the prospects for scaling the algorithm up to realistically sized systems are very good. Nash equilibria are investigated assuming all participants attempt to maximize their individual welfare. This is done by iteratively solving the individual welfare maximization algorithm until all individuals stop modifying their bids.     

An automated laser system for eye surgery

The use of lasers in ophthalmology is discussed, and the procedure and requirements of ophthalmic laser surgery are described. An overview of a proposed laser system for eye surgery is given, and its operation is described. Progress and research to date are reviewed.     

An annual framework for clustering-based pricing for an electricity retailer

In the competitive environment, it is necessary for a retailer to increase his/her profit as much as possible. There are few researches focused on the subjects related to the retailer and the retail market. In addition, those researches have mostly focused on the participation of the retailer in the wholesale market. In order to determine the optimal selling price, the knowledge of how and when consumers use electricity is essential to the retailer. This type of information can be found in load profiles of customers. In this paper, an annual framework for optimal price offering by a retailer is proposed which is based on clustering technique. For this purpose, load profiles of customers are used as their consumption patterns. Also, a profit function is defined as the objective of optimization problem based on the load profile considering conditional value at risk (CVaR) for risk modeling. Also, a new acceptance function is proposed to overcome drawbacks of the traditional ones. The objective function is a mixed-integer nonlinear problem which is solved by GAMS software.     

An automated learning system for Java programming

The increase in the numbers enrolling in university computer courses makes huge demands on resources, and maintaining standards of teaching and tutorial support is almost impossible without a massive increase in staff. To overcome this situation, the authors have developed RoboProf, an automated learning environment which, as well as generating and assessing programming exercises, provides ongoing assistance and feedback to students without extra demands on lecturer and tutors' time. This system also contains a technique for detecting plagiarism, an increasing problem in computing courses worldwide. For this research, RoboProf was used to teach Java programming to a class containing nearly 300 students in the first year of a computing degree. Use of the system by students was monitored and recorded on log files in order to investigate the extent to which usage patterns influence achieved programming skill. An analysis shows that students who complete the set of RoboProf exercises perform significantly better than those who do not. The timeliness in which these exercises are completed relative to other students is significant: early solvers get higher marks, and students solving the problems with fewer attempts get higher marks. Not surprisingly, plagiarists achieve a lower score than those who do their own work. Other factors that were found to influence programming performance included entry standards and gender. Entry qualifications impacted positively on performance, and males performed significantly better than females. There was a significant positive correlation between the score achieved in the RoboProf course and the performance in a subsequent computing course administered in the traditional manner.     

An automated microwave measurements experience for undergraduates

Experiments in microwave measurements for an undergraduate laboratory course are described that bridge the gap between point-by-point manual measurements and automatic network analyzer measurements. In the sequence of 10 laboratory experiments, the basic parameters of frequency versus wavelength in a waveguide are explored, first in point-by-point measurements versus frequency. Swept frequency measurements are taken up next in the form of an open-ended design experiment investigating a simple two-pole band pass filter. Following this, scalar transmission and reflection measurements are made using a PC as a controller and data-acquisition unit. Lastly, the Hewlett-Packard 8510B Automatic Network Analyzer is used by the students to perform the measurements on the same device as a verification of the earlier results. This paper concentrates on the design of the computer-aided measurements experiment, describing the requirements for the experimental content as well as the peripheral content, such as the controller language (HP BASIC) and the interface (HPIB-HP Interface Bus)     

Coordination between medium-term generation planning and short-term operation in electricity markets

This paper analyzes the coordination between medium-term generation planning and short-term operation in electricity markets. This coordination is particularly important from a practical point of view in order to guarantee that certain aspects of the operation that arise in the medium-term level are explicitly taken into account: limited-energy resources and obligatory-use resources. Three different approaches are proposed in order to guarantee that short-term decisions made by a generation company are consistent with its operation objectives formulated from a medium-term perspective. These approaches make use of technical and economic signals to coordinate both time scopes: primal information, dual information, and resource-valuation functions. This paper presents the main advantages and drawbacks of the three approaches and applies them to a case study that uses a conjectural-variation-based representation of the market.     

An automated feedback system for computer organization projects

This paper describes a system, built and refined over the past five years, that automatically analyzes student programs assigned in a computer organization course. The system tests a student's program, then e-mails immediate feedback to the student to assist and encourage the student to continue testing, debugging, and optimizing his or her program. The automated feedback system improves the students' learning experience by allowing and encouraging them to improve their program iteratively until it is correct. The system has also made it possible to add challenging parts to each project, such as optimization and testing, and it has enabled students to meet these challenges. Finally, the system has reduced the grading load of University of Michigan's large classes significantly and helped the instructors handle the rapidly increasing enrollments of the 1990s. Initial experience with the feedback system showed that students depended too heavily on the feedback system as a substitute for their own testing. This problem was addressed by requiring students to submit a comprehensive test suite along with their program and by applying automated feedback techniques to help students learn how to write good test suites. Quantitative iterative feedback has proven to be extremely helpful in teaching students specific concepts about computer organization and general concepts on computer programming and testing.     

基于BP神经网络的短期负荷预测建模仿真

通过BP神经网络与Matlab相结合,建立起三层四功能单元的BP神经网络短期负荷预测模型,并采用某条线路1年的历史负荷波动数据对模型进行“学习”训练。预测一日24小时负荷数据的Matlab仿真及误差分析结果表明,所构筑的BP神经网络模型具有较高的可靠性和准确性,误差率可以有效地控制在2％以内。BP神经网络模型大大提高了短期负荷预测数据的处理效率与可信性,是研究电力系统经济调度的一种新的非线性建模仿真模型。     

考虑负荷周期性和变化率的短期电价预测

为了提高电价预测精确度以提高其实用价值,在电价预测模型中引入负荷周期性和变化率因素.根据负荷对电价的影响建立基于系统负荷的短期电价预测模型,使用小波分解对负荷和电价数据进行分析处理,采用神经网络的预测方法对短期市场清算电价进行预测.考虑负荷和电价的周期特性,在预测模型输入侧增加了负荷的周期性因素.考虑负荷剧变引起的电价变化,定义综合负荷变化率影响因素并加入模型输入侧来提高预测精确度.预测实例采用实际负荷值为输入,其结果表明引入负荷周期特性和综合负荷变化率因素后预测相对预测误差和单点最大预测误差分别降低35%和28%,有效地提高了模型的预测精确度.     

An improved PSO technique for short-term optimal hydrothermal scheduling

This paper presents a new approach to the solution of optimal power generation to short-term hydrothermal scheduling problem, using improved particle swarm optimization (IPSO) technique. The practical hydrothermal system is highly complex and possesses nonlinear relationship of the problem variables, cascading nature of hydraulic network, water transport delay and scheduling time linkage that make the problem of finding global optimum difficult using standard optimization methods. In this paper an improved PSO technique is suggested that deals with an inequality constraint treatment mechanism called as dynamic search-space squeezing strategy to accelerate the optimization process and simultaneously, the inherent basics of conventional PSO algorithm is preserved. To show its efficiency and robustness, the proposed IPSO is applied on a multi-reservoir cascaded hydro-electric system having prohibited operating zones and a thermal unit with valve point loading. Numerical results are compared with those obtained by dynamic programming (DP), nonlinear programming (NLP), evolutionary programming (EP) and differential evolution (DE) approaches. The simulation results reveal that the proposed IPSO appears to be the best in terms of convergence speed, solution time and minimum cost when compared with established methods like EP and DE.     

An integrated simulation model for European electricity and natural gas supply

Contents This paper describes an empirical long-run simulation model for the European electricity and natural gas market. In a first step, electricity and gas markets are modelled separately by dynamic linear programming. In a second step, the models are linked by iteration. The model results show significant interrelationships between gas consumption in power generation and supply conditions on the gas market. Received: 30 April 2001 / Accepted: 7 June 2001     

An optimized procedure for determining incremental heat ratecharacteristics

An optimized procedure for producing generator incremental heat rate curves from continually sampled unit performance data is described. A generalized reduced-gradient algorithm is applied to optimally locate break points in incremental heat rate curves. Advantages include the ability to automatically take into consideration slow time-varying effects such as unit aging and temperature variations in combustion air and cooling water. The procedure is tested using actual fuel rate data for four generators     

An aggregate Weibull approach for modeling short-term system generating capacity

Deregulation of electricity markets is occurring all over the world. This trend introduces new risks and uncertainties into the electricity industry, the most significant being price risk. The spot price of electricity is highly volatile, and the ability to price risk management contracts on this commodity is contingent on a robust and realistic model of the underlying price process. One key driver of electricity spot price is the forced outages of generating plants in the system. The current paper describes a system aggregate model of short-term generating capacity that can be adapted to any generating system of interest. After describing the model, we test it using the IEEE Reliability Test System (RTS).     

基于小波分解和相同尺度序列的神经网络短期电价预测

基于小波分解的短期电价预测主要是对电价样本分解后的各个子序列进行预测,并重构各个预测结果得到最终预测电价。在这一基础上,对电价和负荷样本进行多分辨率小波分解至2尺度,然后剔除噪声信号,再将其中相同制度的电价和负荷子序列相结合,并根据该尺度的时频特征设计建立神经网络模型进行预测,最后将各个子序列的预测结果重构得到预测电价。在算例分析中采用PJM市场2007年3月至2008年2月的数据,并通过绘制误差持续曲线,测试对比本文提出的预测方法和其他预测模型,证明了该方法的有效性和可行性。     

电力市场条件下供电公司最优购电问题研究

电力市场条件下,根据现代投资理论建立了购电公司最优购电分配模型,用以确定远期合同市场、日前市场、实时市场的购电量,利用蚁群算法对模型求解。给出具体算例,通过算例证明了模型的正确性与可行性。