Electricity market price spike analysis by a hybrid data model and feature selection technique

In a competitive electricity market, energy price forecasting is an important activity for both suppliers and consumers. For this reason, many techniques have been proposed to predict electricity market prices in the recent years. However, electricity price is a complex volatile signal owning many spikes. Most of electricity price forecast techniques focus on the normal price prediction, while price spike forecast is a different and more complex prediction process. Price spike forecasting has two main aspects: prediction of price spike occurrence and value. In this paper, a novel technique for price spike occurrence prediction is presented composed of a new hybrid data model, a novel feature selection technique and an efficient forecast engine. The hybrid data model includes both wavelet and time domain variables as well as calendar indicators, comprising a large candidate input set. The set is refined by the proposed feature selection technique evaluating both relevancy and redundancy of the candidate inputs. The forecast engine is a probabilistic neural network, which are fed by the selected candidate inputs of the feature selection technique and predict price spike occurrence. The efficiency of the whole proposed method for price spike occurrence forecasting is evaluated by means of real data from the Queensland and PJM electricity markets.     

A long-term risk management tool for electricity markets using swarm intelligence

This paper addresses the optimal involvement in derivatives electricity markets of a power producer to hedge against the pool price volatility. To achieve this aim, a swarm intelligence meta-heuristic optimization technique for long-term risk management tool is proposed. This tool investigates the long-term opportunities for risk hedging available for electric power producers through the use of contracts with physical (spot and forward contracts) and financial (options contracts) settlement. The producer risk preference is formulated as a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance of return and the expectation are based on a forecasted scenario interval determined by a long-term price range forecasting model. This model also makes use of particle swarm optimization (PSO) to find the best parameters allow to achieve better forecasting results. On the other hand, the price estimation depends on load forecasting. This work also presents a regressive long-term load forecast model that make use of PSO to find the best parameters as well as in price estimation. The PSO technique performance has been evaluated by comparison with a Genetic Algorithm (GA) based approach. A case study is presented and the results are discussed taking into account the real price and load historical data from mainland Spanish electricity market demonstrating the effectiveness of the methodology handling this type of problems. Finally, conclusions are dully drawn.     

RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

With the appearance of electricity markets, the variation of the price of electricity will influence usage custom of electric energy. This will complicate short-term load forecasting and challenge the existing forecasting methods that are applied to a fixed-price environment. In regard to the influence of real-time electricity prices on short-term load, a model to forecast short-term load is established by combining the radial basis function (RBF) neural network with the adaptive neural fuzzy inference system (ANFIS). The model first makes use of the nonlinear approaching capacity of the RBF network to forecast the load on the prediction day with no account of the factor of electricity price, and then, based on the recent changes of the real-time price, it uses the ANFIS system to adjust the results of load forecasting obtained by RBF network. This system integration will improve forecasting accuracy and overcome the defects of the RBF network. As shown in this paper by the results of an example of factual forecasting, the model presented can work effectively.      

Price forecasting and validation in the Spanish electricity market using forecasts as input data

The electricity sector has been subjected to major changes in the last few years. Previously, there existed a regulated system where electric companies could know beforehand the amount of energy each generator would produce, hence basing their largely operational strategy on cost minimization in order to increase their profits. In Spain, from 1988 till 1997, electricity prices were established by the ‘Marco Legal Estable’ – Stable Legal Framework –, where the Ministry of Industry and Energy acknowledged the existence of certain generation costs related to each type of technology. It was an industrial sector with no actual competition and therefore, with very few controllable risks. In the aftermath of the electricity market liberalization competition and uncertainty arose. Electricity spot prices became highly volatile due to the specific characteristics of electricity as a commodity. Long-term contracts allowed for hedge funds to act against price fluctuation in the electricity market. As a consequence, developing an accurate electricity price forecasting model is an extremely difficult task for electricity market agents. This work aims to propose a methodology to improve the limitations of those methodologies just using historical data to forecast electricity prices. In this manner, and in order to gain access to more recent data, instead of using natural gas prices and electricity load historical data, a regression model to forecast the evolution of natural gas prices, and a model based on artificial neural networks (ANN) to forecast electricity loads, are proposed. The results of these models are used as input for an electricity price forecast model. Finally, and to demonstrate the effectiveness of the proposed methodology, several study cases applied to the Spanish market, using real price data, are presented.     

基于小波包分解和长短期记忆网络的短期电价预测

在电力市场环境下,精准的短期电价预测可以保障电网优化调度和安全稳定运行,但实时电价具有非平稳性和非线性的特点,加大了预测难度。针对这一问题,提出了一种基于小波包分解（WPD）和长短期记忆（LSTM）网络的短期实时电价预测方法。将实时电价序列分解,得到最高频细节部分和低频趋势部分,剔除波动性高、无效信息多的高频细节部分,再采用LSTM网络对有效信息最多、更能体现电价序列的趋势部分进行实时电价预测。使用所提方法对美国PJM市场某地区实时电价数据进行预测实验,结果表明所提方法相比随机森林、BP神经网络、支持向量机电价预测方和传统的LSTM网络电价预测方法具有更高预测精度。     

含误差预测校正的ARIMA电价预测新方法

在电力市场中，准确的电价预测是各市场参与方共同关心的重要问题。已经提出多种理论和方法尝试提高电价预测精度，然而由于影响电价的因素十分复杂，实践证明靠建立单一的电价预测模型来提高预测精度是非常困难的。该文在分析电价波动特性和现有预测方法的基础上，首次提出结合误差预测校正电价预测来提高预测精度的新思路。在建立常规电价预测模型的基础上，对预测后的残差形成的随机序列也迭代地建立预测模型，并用预测的误差修正电价预测结果。该文采用ARIMA方法建立电价预测和误差预测模型，并用加州电力市场的历史数据建立基于ARIMA的日平均电价预测模型，预测结果表明所提方法能明显改善预测精度，而且方法简捷明了，能够推广到小时电价预测、负荷预测和其它预测领域。     

基于稀疏自编码非线性自回归网络的负荷电价预测方法

准确的电价和负荷预测对现代电力系统至关重要,但由于电价与负荷之间存在较强的相关性,若不考虑其相互影响,将导致预测的精度下降。为了提高现有方法的预测准确性,在考虑价格与负荷关系的前提下,提出了一种基于深度递归神经网络的价格与负荷预测模型,即基于外部输入的稀疏自编码器的非线性自回归网络,其功能包括特征提取和预测。首先针对特征提取环节,对原有方法进行改进,提出了稀疏自编码器,可以大大提高特征提取的有效性。其次,利用非线性自回归网络进行电价和负荷预测。使用电力市场大数据ISONE和PJM进行仿真验证,与级联Elam网络相比,ESAENARX在负荷预测方面将平均绝对误差降低了16%,在价格预测方面降低了7%。     

Short‐term electricity load and price forecasting based on clustering and next symbol prediction

Short‐term electricity load and price forecasting is an important issue in competitive electricity markets. In this paper, we propose a new direct time series forecasting method based on clustering and next symbol prediction. First, the cluster label sequence is obtained from time series clustering. Then a lossless compression algorithm of prediction by partial match version C coder (PPMC) is applied on this obtained discrete cluster label sequence to predict the next cluster label. Finally, the whole time series values of one‐step‐ahead can be directly forecast from the predicted cluster label. The proposed method is evaluated on electricity time series datasets, and the numerical experiments show that the proposed method can achieve promising results in day‐ahead electricity load and price forecasting. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

电力市场中的价格钉研究

电力市场中普遍存在价格钉现象。结合近年来的相关文献对价格钉进行了综述：首先分析自由竞争的电力市场中电价的基本特点,并从经济学角度分析价格钉产生的原因,影响价格钉的供给与需求方面的主要因素,然后重点对价格钉的预测方法和控制途径进行了评述,对比了各种预测方法的优缺点及适用面,以及各种可能的控制途径。文章最后探讨了今后的研究方向。     

Electricity price forecasting using artificial neural networks

Electricity price forecasting in deregulated open power markets using neural networks is presented. Forecasting electricity price is a challenging task for on-line trading and e-commerce. Bidding competition is one of the main transaction approaches after deregulation. Forecasting the hourly market-clearing prices (MCP) in daily power markets is the most essential task and basis for any decision making in order to maximize the benefits. Artificial neural networks are found to be most suitable tool as they can map the complex interdependencies between electricity price, historical load and other factors. The neural network approach is used to predict the market behaviors based on the historical prices, quantities and other information to forecast the future prices and quantities. The basic idea is to use history and other estimated factors in the future to “fit” and “extrapolate” the prices and quantities. A neural network method to forecast the market-clearing prices (MCPs) for day-ahead energy markets is developed. The structure of the neural network is a three-layer back propagation (BP) network. The price forecasting results using the neural network model shows that the electricity price in the deregulated markets is dependent strongly on the trend in load demand and clearing price.     

Day-ahead price forecasting in restructured power systems using artificial neural networks

Over the past 15 years most electricity supply companies around the world have been restructured from monopoly utilities to deregulated competitive electricity markets. Market participants in the restructured electricity markets find short-term electricity price forecasting (STPF) crucial in formulating their risk management strategies. They need to know future electricity prices as their profitability depends on them. This research project classifies and compares different techniques of electricity price forecasting in the literature and selects artificial neural networks (ANN) as a suitable method for price forecasting. To perform this task, market knowledge should be used to optimize the selection of input data for an electricity price forecasting tool. Then sensitivity analysis is used in this research to aid in the selection of the optimum inputs of the ANN and fuzzy c-mean (FCM) algorithm is used for daily load pattern clustering. Finally, ANN with a modified Levenberg–Marquardt (LM) learning algorithm are implemented for forecasting prices in Pennsylvania–New Jersey–Maryland (PJM) market. The forecasting results were compared with the previous works and showed that the results are reasonable and accurate.     

考虑市场力的短、中、长期电价预测

分别介绍了采用BP神经网络模型和线性回归模型进行电价预测的方法和结果。方法的突出特点是在预测模型中引入了一个衡量市场力的新指标——发电容量必须运行率(MRR)，从而充分考虑了市场力对电价的影响，提高了电价预测的精度，特别是增强了短期预测模型对最高限价的预测能力。文中对MRR指标进行了简单的介绍，并针对电价预测的不同特点，对预测模型和预测变量的选择进行了探讨，提出了自己的观点。基于浙江电力市场实际运营数据的初步预测结果表明，所建预测模型是适用的，选择的预测输入变量是恰当的，电价预测精度能够满足电力市场实际运营的需要。     

电力系统负荷预测与电价预测

讨论了电力系统负荷预测与电价预测的特点 ,并且对二者之间的异同点进行了说明和分析 ,对电价预测的意义及方法进行了初步的探讨。     

基于减法聚类及自适应模糊神经网络的短期电价预测

提出了基于Takagi-Sugeno模型的自适应模糊神经网络的短期电价预测方法。首先采用减法聚类方法确定自适应模糊神经网络的结构,然后利用混合学习算法训练该网络的前件参数和结论参数,最后将影响未来日电价的相关因素输入到训练好的自适应模糊神经网络中进行电价预测。以美国加州电力市场公布的1999年负荷与电价数据进行模型训练和预测,结果表明采用该方法所建立的预测模型具有较高的预测精度。     

考虑多重周期性的短期电价预测

考虑到电价各时段变化以及周末与工作日变化的差异,提出了区分周末的分时段短期电价预测模型。该模型首先将各日中同一时段的电价形成该时段的电价序列,再将各时段电价序列分为工作日电价序列和周末电价序列。这样形成了多个消除了日周期性和星期周期性的子电价序列,分别对各子电价序列进行预测以得到预测日电价。采用基于小波分析的广义回归神经网络对这些子电价序列分别进行提前一天的预测,各子电价序列的预测电价就形成了下一天的预测电价。采用该方法对西班牙电力市场电价进行了长时间的连续预测,并与已有的预测方法进行了详细的比较分析,研究表明该方法能够提供更准确的预测电价。     

基于Attention-GRU的短期电价预测

通过分析得出电价与负荷具有相关性,因此在电价预测模型中需要考虑实时负荷的影响。在此基础上针对前馈神经网络不能处理时序数据的缺陷与LSTM神经网络预测速度慢的问题,提出了一种基于Attention-GRU (Attention gated recurrent unit, Attention-GRU)的实时负荷条件下短期电价预测模型。该模型充分利用电价的时序特性,并采用Attention机制突出了对电价预测起关键性作用的输入特征。以美国PJM电力市场实时数据为例进行分析,通过与其他几种预测模型相比,验证了该方法具有更高的预测精度;与LSTM神经网络相比具有更快的预测速度。     

基于时间序列ARMAX模型的短期电价预测方法

电力市场环境下,准确的电价预测可为市场参与者制定合理的竞争策略提供重要的参考信息.在对美国PJM电力市场日前电价的各种影响因素和波动规律综合分析的基础上,建立了一个基于ARMAX考虑负荷与电价之间非线性关系的短期电价预测模型.对PJM电力市场2008年1月到4月的历史数据的算例研究表明,该方法能够准确反映电价的变化规律,具有较高的预测准确性.     

基于CEEMDAN分解的深度学习分位数回归电价预测

电力市场中电价预测的准确性对于供应商竞价策略的制定至关重要。针对电价预测问题,提出一种基于完备经验模态分解的深度学习分位数回归电价预测方法。首先,采用自适应噪声的完备经验模态分解方法对电价序列进行分解,得出各个模态分量;然后,采用深度学习中空洞因果卷积神经网络预测模型在不同分位数下对各个分量进行预测,并将预测结果重构;最后,对预测结果采用核密度估计得到电价的概率密度函数。经过对美国电力市场PJM的实际数据进行仿真验证,所提出的组合预测方法相比于其他分位数回归方法,不仅具有更高的预测准确度,且可以为供电商提供更多有效信息。     

几种灰色模型用于电力消费中期预测研究

根据三类产业与居民生活用电量在用电方式、用电增长趋势等方面的不同特点,对灰色ＧＭ（１,１）模型、改进的ＧＭ（１,３）模型以及关联多因子模型在地区用电量和最高用电负荷预测中的应用做了初步研究。以上海市１９９０～１９９８年的电力资料和经济资料为基础,应用几中灰色模型对三类产业用电量、居民生活用电量及最高用电负荷进行了预测,并将几种模型的预测 结果进行了比较。结果表明,不同模型适用于不同特点的电力消费预     

数据挖掘技术在电价预测中的应用

简要叙述了数据挖掘技术的特点,分析了影响电价的因素,提出了一种基于数据挖掘技术的电价预测方法。该方法将电价用市场供求关系、上网竞价发电功率、用户负荷需求、燃料价格、物价指数和消费水平等元素来表征,并考虑了不同电价影响因子的影响程度。利用数据挖掘中的相似性搜索技术,引进权重系数对所搜索到的匹配电价序列进行加权平均,进而得到所预测的电价值。最后举例说明了该方法的具体应用过程。