Electricity Price Forecasting Based on AOSVR and Outlier Detection

Electricity price is of the first consideration for all the participants in electric power market and its characteristics are related to both market mechanism and variation in the behaviors of market participants. It is necessary to build a real-time price forecasting model with adaptive capability; and because there are outliers in the price data, they should be detected and filtrated in training the forecasting model by regression method. In view of these points, mis paper presents an electricity price forecasting method based on accurate on-line support vector regression (AOSVR) and outlier detection. Numerical testing results show that the method is effective in forecasting the electricity prices in electric power market

A Short-Term Electricity Price Forecasting Scheme for Power Market

Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.

Comparison of ARIMA and ANN Models Used in Electricity Price Forecasting for Power Market

In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Alternative techniques for forecasting mineral commodity prices

Forecasting mineral commodity(MC) prices has been an important and difficult task traditionally addressed by econometric, stochastic-Gaussian and time series techniques. None of these techniques has proved suitable to represent the dynamic behavior and time related nature of MC markets. Chaos theory(CT) and machine learning(ML) techniques are able to represent the temporal relationships of variables and their evolution has been used separately to better understand and represent MC markets. CT can determine a system's dynamics in the form of time delay and embedding dimension. However, this information has often been solely used to describe the system's behavior and not for forecasting.Compared to traditional techniques, ML has better performance for forecasting MC prices, due to its capacity for finding patterns governing the system's dynamics. However, the rational nature of economic problems increases concerns regarding the use of hidden patterns for forecasting. Therefore, it is uncertain if variables selected and hidden patterns found by ML can represent the economic rationality.Despite their refined features for representing system dynamics, the separate use of either CT or ML does not provide the expected realistic accuracy. By itself, neither CT nor ML are able to identify the main variables affecting systems, recognize the relation and influence of variables though time, and discover hidden patterns governing systems evolution simultaneously. This paper discusses the necessity to adapt and combine CT and ML to obtain a more realistic representation of MC market behavior to forecast long-term price trends.

Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference

This paper presents an artificial neural network, ANN, based approach for estimating short-term wholesale electricity prices using past price and demand data. The objective is to utilize the piecewise continuous na-ture of electricity prices on the time domain by clustering the input data into time ranges where the variation trends are maintained. Due to the imprecise nature of cluster boundaries a fuzzy inference technique is em-ployed to handle data that lies at the intersections. As a necessary step in forecasting prices the anticipated electricity demand at the target time is estimated first using a separate ANN. The Australian New-South Wales electricity market data was used to test the system. The developed system shows considerable im-provement in performance compared with approaches that regard price data as a single continuous time se-ries, achieving MAPE of less than 2% for hours with steady prices and 8% for the clusters covering time pe-riods with price spikes.

Data-driven Two-step Day-ahead Electricity Price Forecasting Considering Price Spikes

In the electricity market environment,electricity price forecasting plays an essential role in the decision-making process of a power generation company,especially in developing the optimal bidding strategy for maximizing revenues.Hence,it is necessary for a power generation company to develop an accurate electricity price forecasting algorithm.Given this background,this paper proposes a two-step day-ahead electricity price forecasting algorithm based on the weighted Knearest neighborhood(WKNN)method and the Gaussian process regression(GPR)approach.In the first step,several predictors,i.e.,operation indicators,are presented and the WKNN method is employed to detect the day-ahead price spike based on these indicators.In the second step,the outputs of the first step are regarded as a new predictor,and it is utilized together with the operation indicators to accurately forecast the electricity price based on the GPR approach.The proposed algorithm is verified by actual market data in Pennsylvania-New JerseyMaryland Interconnection(PJM),and comparisons between this algorithm and existing ones are also made to demonstrate the effectiveness of the proposed algorithm.Simulation results show that the proposed algorithm can attain accurate price forecasting results even with several price spikes in historical electricity price data.

An integrated new threshold FCMs Markov chain based forecasting model for analyzing the power of stock trading trend

This paper explores the power of stock trading trend using an integrated New ThresholdFuzzy Cognitive Maps(NTFCMs)Markov chain model.This new model captures thepositive as well as the negative jumps and predicts the trend for different stocks over 4months which follow an uptrend,downtrend and a mixed trend.The mean absolute percent error(MAPE)tolerance limits,the root mean square error(RMSE)tolerance limits aredetermined for various stock indices over a multi-timeframe period and observed for theexisting methods lying within the defined limits.The results show for every‘n’number ofpredictions made,the predicted close value of the day’s stock price was within tolerancelimit with 0%error and with 100%accuracy in predicting the future trend.

Day-ahead electricity price forecasting using back propagation neural networks and weighted least square technique

This paper proposes the day-ahead electricity price forecasting using the artificial neural networks （ANN） and weighted least square （WLS） technique in the restructured electricity markets. Price forecasting is very important for online trading, e-commerce and power system operation. Forecasting the hourly locational marginal prices （LMP） in the electricity markets is a very important basis for the decision making in order to maximize the profits/benefits. The novel approach pro- posed in this paper for forecasting the electricity prices uses WLS technique and compares the results with the results obtained by using ANNs. To perform this price forecasting, the market knowledge is utilized to optimize the selection of input data for the electricity price forecasting tool. In this paper, price forecasting for Pennsylvania-New Jersey-Maryland （PJM） interconnec- tion is demonstrated using the ANNs and the proposed WLS technique. The data used for this price forecasting is obtained from the PJM website. The forecasting results obtained by both methods are compared, which shows the effectiveness of the proposed forecasting approach. From the simulation results, it can be observed that the accuracy of prediction has increased in both seasons using the proposed WLS technique. Another important advantage of the proposed WLS technique is that it is not an iterative method.

Short-Term Electricity Price Forecasting Using Random Forest Model with Parameters Tuned by Grey Wolf Algorithm Optimization

Accurately forecasting short-term electricity prices is of great significance to electricity market participants.Compared with the time series forecasting methods,machine learning forecasting methods can consider more external factors.The forecasting accuracy of machine learning models is greatly affected by the parameters,meanwhile,the manual selection of parameters usually cannot guarantee the accuracy and stability of the forecasting.Therefore,this paper proposes a random forest(RF)electricity price forecasting model based on the grey wolf optimizer(GWO)to improve the accuracy of forecasting.Among them,RF has a good ability to deal with the problem of non-linear and unstable electricity prices.The optimization of model parameters by GWO can overcome the instability of the forecasting accuracy of manually tune parameters.On this basis,the short-term electricity prices of the PJM power market in four seasons are separately predicted.Experimental results show that the RF algorithm can better predict the short-term electricity price,and the optimization of the RF forecasting model by GWO can effectively improve the accuracy of the RF forecasting model.

An Application of Decision Trees Algorithm to Project Hourly Electricity Spot Price as Support for Decision Making on Electricity Trading in Brazil

Estimating the price of a financial asset or any tradable product is a complex task that depends on the availability of a reasonable amount of data samples. In the Brazilian electricity market environment, where spot prices are centrally calculated by computational models, the projection of hourly energy prices at the spot market is essential for decision-making, and with the particularities of this sector, this task becomes even more complex due to the stochastic behavior of some variables, such as the inflow to hydroelectric power plants and the correlation between variables that affect electricity generation, traditional statistical techniques of time series forecasting present an additional complexity when one tries to project scenarios of spot prices on different time horizons. To address these complexities of traditional forecasting methods, this study presents a new approach based on Machine Learning methodology applied to the electricity spot prices forecasting process. The model’s Learning Base is obtained from public information provided by the Brazilian official computational models: NEWAVE, DECOMP, and DESSEM. The application of the methodology to real cases, using back-testing with actual information from the Brazilian electricity sector demonstrates that the research is promising, as the adherence of the projections with the realized values is significant.

我国蔬菜产业市场运行态势研究

近10年来,我国蔬菜种植面积、产量逐年增加,消费需求也明显上升,总供给和总需求基本平衡,市场运行总体比较稳健。蔬菜市场运行具有季节性波动、产地转换等五大特征。2023年,我国蔬菜市场产销两旺,市场价格高位运行,农业农村部重点监测的28种蔬菜全国批发价格全年平均是近10年来的最高价;展望2024年,蔬菜总供给和总需求基本平衡,略有结余。蔬菜市场主要面临气候变化、种植意愿、产销衔接等五大风险点。建议今后要进一步强化“菜篮子”建设、蔬菜地产地销、均衡上市、监测预警和政策扶持,努力实现蔬菜产业保供稳价和高质量发展。

考虑时序二维变化的日前市场电价预测模型

电价预测对电力市场参与者的运营决策及电力系统安全稳定运行关系重大。针对日前市场电价预测问题,本文提出一种考虑时序二维变化的日前市场电价预测模型和方法。首先采用改进的带自适应噪声的完全集成经验模式分解对日前市场电价历史数据进行分解,然后对其高、低频子序列分别采用考虑时序二维变化的Ti⁃mesNet和基于统计分析的差分自回归移动平均进行预测,二者结果之和构成日前市场电价的预测值。仿真结果表明,所提方法相较于现有单一或组合模型具有较高的预测精度。

Price-taker在两个电力市场中的交易决策 (一)购电商的策略

多市场交易量分配问题是电力市场参与者竞标决策中的重要问题之一。研究了考虑电价风险的情况下,Price—taker购电商在两个市场之间的交易量优化问题。讨论了购电商在两市场中优化分配问题的建模及解析解,引入风险因子概念,并用条件概率模型反映电价的不确定性,目标是总费用及风险均较低。用美国加州电力市场的实际数据进行了仿真。

沙戈荒基地外送全环节经济性测算及市场竞争力分析

在风光火储打捆基地外送价格形成机制尚不明确且暂无可借鉴成熟经验的背景下,对沙戈荒基地外送经济性测算展开分析与讨论。首先考虑以系统运行成本最小为目标搭建了生产运行模拟模型,并基于经营期法和按发电量比例分配原则搭建打捆送电成本电价测算模型。以沙戈荒基地外送某典型工程为例,对基地发电、送电等外送全环节经济性进行了测算。进一步从打捆电源配置、标准煤单价、增配储能、考虑调峰收益、单位造价等几方面分析影响沙戈荒基地发电成本电价的主要因素,并通过比较受端区域燃煤发电基准价与测算的落地成本电价对市场竞争力进行分析讨论。案例的分析结果从经济成本与市场竞争力角度对沙戈荒基地外送工程规划、建设及定价机制设计具有借鉴作用,对全面贯彻落实碳达峰碳中和战略决策,服务能源转型和清洁绿色低碳发展具有重大意义。

考虑电力行业碳排放的全国碳价预测

为更好预测全国碳价走势,基于带有外生变量的自回归差分移动平均模型(autoregressive integrated moving average with exogenous variable model,ARIMAX),分履约期和非履约期使用不同的外生变量分别构建了全国碳价预测模型。首先,基于对全国碳市场制度规则研究和交易特征分析,识别出全国碳价在非履约期主要受参与者预期的影响,在履约期碳价主要受企业履约需求驱动;其次,在模型训练方面,采用一种自回归差分移动平均模型,在不同阶段引入不同的外生变量来提升碳价预测效果;最后,基于全国碳市场第一履约期真实价格数据验证结果表明,所提的全国碳价预测模型在准确性方面优于基准模型。

注册制改革对创业板IPO定价效率的影响

基于2018年8月24日至2022年11月24日期间数据,运用混合截面双重差分模型进行实证检验,验证注册制改革对创业板IPO定价效率的影响。实证结果表明,创业板注册制改革在短期内催涨“炒新”投机情绪,抑制了创业板IPO的定价效率;但从长远来看,注册制改革显示出积极的激励作用。异质性检验结果表明,注册制改革在低承销商声誉组和低筹资金额组样本中发挥更为显著的正向激励作用。上述结论说明注册制改革并未解决信息不对称问题,承销商功能仍未得到市场和投资者认可。鉴于此,建议政府和监管机构完善定价机制及引导投资者结构优化,以遏制短期内的投机行为,确保注册制改革维护市场稳定的长期目标得以实现。

Modelling the iron ore price index:A new perspective from a hybrid data reconstructed EEMD-GORU model

As iron ore is the fundamental steel production resource,predicting its price is strategically important for risk management at related enterprises and projects.Based on a signal decomposition technology and an artificial neural network,this paper proposes a hybrid EEMD-GORU model and a novel data reconstruction method to explore the price risk and fluctuation correlations between China's iron ore futures and spot markets,and to forecast the price index series of China's and international iron ore spot markets from the futures market.The analysis found that the iron ore futures market in China better reflected the price fluctuations and risk factors in the imported and international iron ore spot markets.However,the forward price in China's iron ore futures market was unable to adequately reflect the changes in the domestic iron ore market,and was therefore unable to fully disseminate domestic iron ore market information.The proposed model was found to provide better market risk perceptions and predictions through its combinations of the different volatility information in futures and spot markets.The results are valuable ref-erences for the early-warning and management of the related enterprise project risks.

售电公司电力现货交易辅助决策系统关键技术研究

对电价预测关键技术、用户负荷预测关键技术和零售套餐设计与测算关键技术进行研究。结合基于XGBDT的电价预测算法与基于人工神经网络的电价预测算法,提出了启发式组合电价预测算法,该算法计算简便、预测准确并且能够进行人工调节。将支持向量回归法用于用户负荷预测,用户负荷预测的精度和效率都较高。建立售电公司电力现货交易辅助决策系统,其功能包括市场分析、出清电价预测、用户负荷预测、现货交易决策、中长期交易管理、零售交易管理等,有助于售电公司降低交易风险,增加现货交易收益。

注册制新股发行市场化改革成效及优化研究——基于市场化定价效率视角

注册制下新股发行市场化定价的合理性,应考察公司首发价格是否反映公司的内在价值、行业前景、成长与发展等关键要素。文章回顾了我国证券市场的发展历程,对三大股票发行制度进行比较。继而以全面注册制改革为背景,研究分析其新股发行定价机制、市场化定价效率现状。通过对影响我国市场化定价效率的诸多因素研究,得出主要影响因素,如新股发行时存在的代理问题、承销商有待加强的定价能力和自我约束能力、机构投资者的非理性交易行为等,进而为我国证券市场发展提出建议:第一,提高注册制下新股发行的信息披露质量;第二,压严压实中介机构责任;第三,提升承销商定价能力与自我约束能力;第四,加强投资者教育和投资者保护。