Long-Term Electrical Load Forecasting in Rwanda Based on Support Vector Machine Enhanced with Q-SVM Optimization Kernel Function

In recent years, Rwanda’s rapid economic development has created the “Rwanda Africa Wonder”, but it has also led to a substantial increase in energy consumption with the ambitious goal of reaching universal access by 2024. Meanwhile, on the basis of the rapid and dynamic connection of new households, there is uncertainty about generating, importing, and exporting energy whichever imposes a significant barrier. Long-Term Load Forecasting (LTLF) will be a key to the country’s utility plan to examine the dynamic electrical load demand growth patterns and facilitate long-term planning for better and more accurate power system master plan expansion. However, a Support Vector Machine (SVM) for long-term electric load forecasting is presented in this paper for accurate load mix planning. Considering that an individual forecasting model usually cannot work properly for LTLF, a hybrid Q-SVM will be introduced to improve forecasting accuracy. Finally, effectively assess model performance and efficiency, error metrics, and model benchmark parameters there assessed. The case study demonstrates that the new strategy is quite useful to improve LTLF accuracy. The historical electric load data of Rwanda Energy Group (REG), a national utility company from 1998 to 2020 was used to test the forecast model. The simulation results demonstrate the proposed algorithm enhanced better forecasting accuracy.

The Efficiency of Markets in Response to Earnings Forecasts: The Case of China

This paper investigates the semi-strong form of efficiency of Chinese stock markets in response to earnings forecast announcement by employing the methodology of event study. The sample period is from January 2009 to January 2018, in total 564 event were examined. The reaction of markets to different types of new announcement is investigated and presented separately. Firstly, examination of positive and negative earnings forecast report shows that information shock brings a significant positive and negative returns during the event window. In addition, analysis of different sub-windows showed prices adjust to news quickly and effectively. However, no news announcements bring no significant shock to market, prices are not impacted by slight change forecasts. In general, empirical results provided evidences of semi-strong market efficiency. Earnings forecast announcements possess huge impact on market prices, therefore participants can make abnormal profit if they act on the information very quickly. However, beyond event window information becomes ineffective and does not possess any kind of content to make above market returns .

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma’an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.

Spatial and temporal synthesized probability gain for middle and long-term earthquake forecast and its preliminary application

The principle of middle and long-term earthquake forecast model of spatial and temporal synthesized probability gain and the evaluation of forecast efficiency (R-values) of various forecast methods are introduced in this paper. The R-value method, developed by Xu (1989), is further developed here, and can be applied to more complicated cases. Probability gains in spatial and/or temporal domains and the R-values for different forecast methods are estimated in North China. The synthesized probability gain is then estimated as an example.

Study of Holocene glacier degradation in central Asia by isotopic methods for long-term forecast of climate changes

This article presents a summary of our studies of Holocene moraines and glaciers of the Tien-Shan, Pamir, and Himalaya moun- mills with the purpose of providing pattern regularity of the Holocene glaciation decomposition. We developed a method for ob- taining reliable radiocarbon dating of moraines with the use of autochthonous organic matter dispersed in fine-grained morainic material, as well there were shown new possibilities of isotope-oxygen and isotope-uranium analysis for the Holocene glaciations dynamics. We found that Holocene glaciations disintegrate stadiaUy according to the decaying principle, and seven main stages may be distinguished. We achieved the absolute dating of the first three stages, identifying these periods as 8,000, 5,000, and 3,400 years ago. The application of the above-mentioned isotope methods of the Holocene glaciations and moraines study will allow re- searchers to improve the offered model of the Holocene glaciations disintegration; it will be great contribution to salvation of the problem of long-term climatic and glaciations forecast.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

Nonlinear Differential Equation of Macroeconomic Dynamics for Long-Term Forecasting of Economic Development

In this article we derive a general differential equation that describes long-term economic growth in terms of cyclical and trend components. Equation is based on the model of non-linear accelerator of induced investment. A scheme is proposed for obtaining approximate solutions of nonlinear differential equation by splitting solution into the rapidly oscillating business cycles and slowly varying trend using Krylov-Bogoliubov-Mitropolsky averaging. Simplest modes of the economic system are described. Characteristics of the bifurcation point are found and bifurcation phenomenon is interpreted as loss of stability making the economic system available to structural change and accepting innovations. System being in a nonequilibrium state has a dynamics with self-sustained undamped oscillations. The model is verified with economic development of the US during the fifth Kondratieff cycle (1982-2010). Model adequately describes real process of economic growth in both quantitative and qualitative aspects. It is one of major results that the model gives a rough estimation of critical points of system stability loss and falling into a crisis recession. The model is used to forecast the macroeconomic dynamics of the US during the sixth Kondratieff cycle (2018-2050). For this forecast we use fixed production capital functional dependence on a long-term Kondratieff cycle and medium-term Juglar and Kuznets cycles. More accurate estimations of the time of crisis and recession are based on the model of accelerating log-periodic oscillations. The explosive growth of the prices of highly liquid commodities such as gold and oil is taken as real predictors of the global financial crisis. The second wave of crisis is expected to come in June 2011.

Short-Term and Long-Term Price Forecasting Models for the Future Exchange of Mongolian Natural Sea Buckthorn Market

Sea buckthorn market floated uncertainly within a narrow range. The market situation provided upward pressure on prices, and producer and consumer interest were poor, coupled with weak prices in the regional markets. The objectives of the study are: 1) to estimate the relationship between wild Sea buckthorn (SB) price and Supply, Demand, while some other factors of crude oil price and exchange rate by using simultaneous Supply-Demand and Price system equation and Vector Error Correction Method (VECM);2) to forecast the short-term and long-term SB price;3) to compare and evaluate the price forecasting models. Firstly, the data was analyzed by Ferris and Engle-Granger’s procedure;secondly, both price forecasting methodologies were tested by Pindyck-Rubinfeld and Makridakis’s procedure. The result shows that the VECM model is more efficient using yearly data;a short-term price forecast decreases, and a long-term price forecast is predicted to increase the Mongolian Sea buckthorn market.

企业竞争战略与管理层策略性业绩:基于管理层业绩预告的分析

从战略管理理论出发研究竞争战略对管理层盈利预测策略的影响。结果表明,采取差异化战略的企业更可能自愿披露盈利预测信息,然而预测偏差较大并更倾向于披露悲观消息。机制检验表明经营风险在差异化战略影响盈利预测中起中介作用。进一步研究发现,在国有企业和金融化程度较强的样本中,差异化竞争战略和盈利预测偏差的关系被显著弱化。

分行业信息披露能提高业绩预告质量吗?——基于准自然实验的证据

分行业信息披露是注册制下高质量监管的重要内容。本文基于沪深交易所分批次颁布行业信息披露指引这一准自然实验构建双重差分模型,实证分析了行业信息披露义务是否改变了公司的策略性信息披露行为。研究发现,要求上市公司披露行业经营性信息能够提高管理层业绩预告质量,且信息透明度、分析师关注度、法制环境、业绩预告属性和经营风险在分行业信息披露对业绩预告质量的影响中发挥着调节作用。会计稳健性和会计信息可比性是潜在机制。本研究有助于理解以行业为属性的信息披露政策对上市公司业绩预告披露行为的影响,同时对完善我国业绩预告制度也有一定的启发。

线上销售能否改善资本市场信息环境——基于分析师盈余预测的证据

基于2015—2019年上市公司通过互联网平台进行线上销售的独特数据,本文研究了线上销售对分析师盈余预测的影响。研究发现:(1)上市公司进行线上销售显著降低了分析师盈余预测误差和分歧度,提升了分析师盈余预测质量,且上述结果在仅保留线上销售行业和PSM匹配后的样本、采用Heckman两阶段模型等方式缓解内生性问题后仍然成立;(2)线上销售对分析师盈余预测质量的提升作用在分析师公开获取信息渠道较少、获取信息成本较高时更为显著;(3)机制检验发现,线上销售可以通过降低盈余波动,或者抑制盈余管理以改善公司盈余信息质量,进而降低分析师盈余预测误差和分歧度。研究结果表明,线上销售不仅增加了分析师获取公司特质信息的来源,而且一定程度上改善了盈余信息质量,有助于分析师发挥资本市场信息中介的重要作用,提高资本市场效率。

MD&A纵向文本相似度与分析师盈余预测准确性

注册制下,信息的真实、准确、完整是投资者合理判断证券价值的关键,分析师在信息作用于股价这一过程中扮演着重要的信息中介角色,而年报所披露的信息对分析师盈余预测至关重要。以2012—2021年沪深A股上市公司为样本,探究了管理层讨论与分析(MD&A)纵向文本相似度对分析师盈余预测准确性的影响。研究发现,MD&A纵向文本相似度越高,分析师盈余预测准确性越低,证实了“增量信息”假说。异质性分析表明,这种影响显著存在于规模小、高科技行业、媒体关注度低的公司中,并且公司位于市场化程度低省份时受此影响更大。进一步研究发现,MD&A纵向文本相似度越高,分析师盈余预测的正向偏差越大。此外,分析师盈余预测准确性的下降会进一步降低资本市场的信息效率。因此,上市公司应重视文本信息披露,避免披露内容样板化;分析师和监管部门应加强对上市公司文本信息披露的监督,优化资本市场信息环境。

分析师的疲劳、预测质量与股票收益——来自中国资本市场的证据

分析师的决策疲劳是否导致预测质量下降是业界关注的问题,本文对中国A股市场中分析师疲劳与预测质量关系进行研究,经过对2006—2023年A股市场卖方分析师个股研究报告中的盈余预测进行数据统计和分析,研究发现:分析师的决策疲劳与盈余预测误差呈正向关系,即随着一周内发布预测的增加,分析师对公司盈余预测误差也在增大。分析师在决策疲劳严重时发布的预测会预示着未来个股中长期收益降低,盈余预测误差在这一关系中充当部分中介变量。决策疲劳引起的较低股票收益与市场信息不对称相关。

Earnings seasonality,management earnings forecasts and stock returns

We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1%around MEFs.Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs.MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns,volume and volatility around EAs.The reduction effects are stronger when MEFs are voluntary or made closer to EAs.The evidence suggests that MEFs facilitate the correction of investors’tendency to extrapolate earnings seasonality and its resulted stock mispricing.

Stochastic Earned Duration Analysis for Project Schedule Management

Earned duration management(EDM)is a methodology for project schedule management(PSM)that can be considered an alternative to earned value management(EVM).EDM provides an estimation of devia-tions in schedule and a final project duration estimation.There is a key difference between EDM and EVM:In EDM,the value of activities is expressed as work periods;whereas in EVM,value is expressed in terms of cost.In this paper,we present how EDM can be applied to monitor and control stochastic pro-jects.To explain the methodology,we use a real case study with a project that presents a high level of uncertainty and activities with random durations.We analyze the usability of this approach according to the activities network topology and compare the EVM and earned schedule methodology(ESM)for PSM.

Skill Assessment of Copernicus Climate Change Service Seasonal Ensemble Precipitation Forecasts over Iran

Medium to long-term precipitation forecasting plays a pivotal role in water resource management and development of warning systems.Recently,the Copernicus Climate Change Service(C3S)database has been releasing monthly forecasts for lead times of up to three months for public use.This study evaluated the ensemble forecasts of three C3S models over the period 1993-2017 in Iran’s eight classified precipitation clusters for one-to three-month lead times.Probabilistic and non-probabilistic criteria were used for evaluation.Furthermore,the skill of selected models was analyzed in dry and wet periods in different precipitation clusters.The results indicated that the models performed best in western precipitation clusters,while in the northern humid cluster the models had negative skill scores.All models were better at forecasting upper-tercile events in dry seasons and lower-tercile events in wet seasons.Moreover,with increasing lead time,the forecast skill of the models worsened.In terms of forecasting in dry and wet years,the forecasts of the models were generally close to observations,albeit they underestimated several severe dry periods and overestimated a few wet periods.Moreover,the multi-model forecasts generated via multivariate regression of the forecasts of the three models yielded better results compared with those of individual models.In general,the ECMWF and UKMO models were found to be appropriate for one-month-ahead precipitation forecasting in most clusters of Iran.For the clusters considered in Iran and for the long-range system versions considered,the Météo France model had lower skill than the other models.