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.

Medium-Term Electric Load Forecasting Using Multivariable Linear and Non-Linear Regression

Medium-term forecasting is an important category of electric load forecasting that covers a time span of up to one year ahead. It suits outage and maintenance planning, as well as load switching operation. We propose a new methodol-ogy that uses hourly daily loads to predict the next year hourly loads, and hence predict the peak loads expected to be reached in the next coming year. The technique is based on implementing multivariable regression on previous year's hourly loads. Three regression models are investigated in this research: the linear, the polynomial, and the exponential power. The proposed models are applied to real loads of the Jordanian power system. Results obtained using the pro-posed methods showed that their performance is close and they outperform results obtained using the widely used ex-ponential regression technique. Moreover, peak load prediction has about 90% accuracy using the proposed method-ology. The methods are generic and simple and can be implemented to hourly loads of any power system. No extra in-formation other than the hourly loads is required.

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.

Medium Term Load Forecasting for Jordan Electric Power System Using Particle Swarm Optimization Algorithm Based on Least Square Regression Methods

This paper presents a technique for Medium Term Load Forecasting (MTLF) using Particle Swarm Optimization (PSO) algorithm based on Least Squares Regression Methods to forecast the electric loads of the Jordanian grid for year of 2015. Linear, quadratic and exponential forecast models have been examined to perform this study and compared with the Auto Regressive (AR) model. MTLF models were influenced by the weather which should be considered when predicting the future peak load demand in terms of months and weeks. The main contribution for this paper is the conduction of MTLF study for Jordan on weekly and monthly basis using real data obtained from National Electric Power Company NEPCO. This study is aimed to develop practical models and algorithm techniques for MTLF to be used by the operators of Jordan power grid. The results are compared with the actual peak load data to attain minimum percentage error. The value of the forecasted weekly and monthly peak loads obtained from these models is examined using Least Square Error (LSE). Actual reported data from NEPCO are used to analyze the performance of the proposed approach and the results are reported and compared with the results obtained from PSO algorithm and AR model.

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.

Using the Analytic Hierarchy Process in Long-Term Load Growth Forecast

The load growth is the most important uncertainties in power system planning process. The applications of the classical long-term load forecasting methods particularly applied to utilities in transition economy are insufficient and may produce incorrect decisions in power system planning process. This paper discusses using the method of analytic hierarchy process to calculate the probability distribution of load growth obtained previously by standard load forecasting methods.

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.

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.

Application and Verification of Multi-Model Products in Medium Range Forecast

The verification analysis is applied to medium-range forecast products of T639, ECMWF, Japan model, NCEP ensemble forecast and NMC multi-model integration in late October 2012. The results show that ECMWF model has obvious advantage over other models in terms of height field and precipitation forecast;the westerly-wind index, geostrophic U wind and 850 hPa temperature prediction products can reflect the adjustment of atmospheric circulation and the activity of cold air, which have a good reference for the medium-range temperature forecast in the eastern China;the prediction of ECMWF height field and wind field can well grasp the main weather processes within 192 h, but beyond 192 h the model forecast ability decreases significantly;different models have large deviations in the medium-range forecast of typhoon track and the intensity and range of typhoon precipitation.

Study on medium-short term earthquake forecast in Yunnan Province by precursory events

The medium-short term forecast for a certain kinds of main earthquake events might be possible with the time-to-failure method presented by Varnes (1989), Bufe and Varnes (1993), which is to simulate an accelerative releasing model of precursory earthquake energy. By fitting the observed data with the theoretical formula, a medium-short term forecast technique for the main shock events could be established, by which the location, time and magnitude of the main shock could be determined. The data used in the paper are obtained from the earthquake catalogue recorded by Yunnan Regional Seismological Network with a time coverage of 1965~2002. The statistical analyses for the past 37 years show that the data of M2.5 earthquakes were fairly complete. In the present paper, 30 main shocks occurred in Yunnan region were simulated. For 25 of them, the forecasting time and magnitude from the simulation of precursory sequence are very close to the actual values with the precision of about 0.57 (magnitude unit). Suppose that the last event of the precursory sequence is known, then the time error for the forecasting main shock is about 0.64 year. For the other 5 main shocks, the simulation cannot be made due to the insufficient precursory events for the full determination of energy accelerating curve or disturbance to the energy-release curve. The results in the paper indicate that there is no obviously linear relation in the optimal searching radius for the main shock and the precursory events because Yunnan is an active region with damage earthquakes and moderate and small earthquakes. However, there is a strong correlation between the main shock moment and the coefficient k/m. The optimal fitting range for the forecasting time and magnitude can be further reduced using the relation between the main shock moment lgM0 and the coefficient lgk/m and the value range of the restricting index m, by which the forecast precision of the simulated main shock can be improved. The time-to-failure method is used to fit 30 main shocks in the paper and more than 80% of them have acquired better results, indicating that the method is prospective for its ability to forecast the known main shock sequence. Therefore, the prospect is cheerful to make medium-short term forecast for the forthcoming main shocks by the precursory events.

Analysis of Forecast and Early Warning of Flood in Medium and Small Rivers

Flooding of small and medium rivers is caused by environmental factors like rainfall and soil loosening.With the development and application of technologies such as the Internet of Things and big data,the disaster supervision and management of large river basins in China has improved over the years.However,due to the frequent floods in small and medium-sized rivers in our country,the current prediction and early warning of small and medium-sized rivers is not accurate enough;it is difficult to realize real-time monitoring of small and medium-sized rivers,and it is also impossible to obtain corresponding data and information in time.Therefore,the construction and application of small and medium-sized river prediction and early warning systems should be further improved.This paper presents an analysis and discussion on flood forecasting and early warning systems for small and medium-sized rivers in detail,and corresponding strategies to improve the effect of forecasting and early warning systems are proposed.

A Hybrid Handover Forecasting Mechanism Based on Fuzzy Forecasting Model in Cellular Networks

As the increasing demand for mobile communications and the shrinking of the coverage of cells, handover mechanism will play an important role in future wireless networks to provide users with seamless mobile communication services. In order to guarantee the user experience, the handover decision should be made timely and reasonably. To achieve this goal, this paper presents a hybrid handover forecasting mechanism, which contains long-term and short-term forecasting models. The proposed mechanism could cooperate with the standard mechanisms, and improve the performance of standard handover decision mechanisms. Since most of the parameters involved are imprecise, fuzzy forecasting model is applied for dealing with predictions of them. The numerical results indicate that the mechanism could significantly decrease the rate of ping-pong handover and the rate of handover failure.

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.

Long-term Energy Demand and CO_2 Problem in the PRC

The long-term energy demand in China and the-Chinese share in global CO2 emission are forecasted on the basis of scenarios of population growth and economy development up to 2050 proposed in view of the interaction of energy, economy, environment and social development. The total energy demand in 2050 will reach 4.4~ 5.4 billion tce. It is shown in energy supply analysis that coal is China’s major energy in primary energy supply. The share of CO2 emission in the future Chinese energy system will be out of proportion to its energy consumption share because of the high persentage of coal to be consumed. It will reach about 27%. The nuclear option which would replace 30.7% of coal in the total primary energy supply will reduce the share by 9.8%. So the policy considerations on the future Chinese energy system is of great importance to the global CO2 issues.

Identification of expected seismic activity areas by forecasting complex seismic-mode parameters in Uzbekistan

In this paper, the author proposed a methodology to reveal expected seismic activation places for coming years by a complex of forecasting parameters of a seismic mode. Areas in Uzbekistan where currently observed anomalies in various parameters of a seismic mode has been revealed. By number of displayed abnormal signs the areas has been ranked based on probability of occurrence of strong earthquakes there. It has prepared schemes of the synoptic forecast of expected seismic activation places in case of occurrence of strong earthquakes in the Central-Asian region.

Long-term Prediction and Verification of Rainfall Based on the Seasonal Model

Using the seasonal cross-multiplication trend model, monthly precipitation of eight national basic weather stations of Shaanxi Province from 2005 to 2010 was predicted, and the forecast results were verified using the rainfall scoring rules of China Meteorological Administration. The verification results show that the average score of annual precipitation prediction in recent six years is higher than that made by a professional forecaster, so this model has a good prospect of application. Moreover, the level of making prediction is steady, and it can be widely used in long-term prediction of rainfall.

Evaluation and Forecasting of Elapsed Fatigue Life of Ship Structures by Analyzing Data from Full Scale Ship Structural Monitoring

This paper describes the activities carried out by CETENA in collaboration with the Italian Navy to assess the behavior of new FREMM frigates by means of an automatic hull monitoring system and to predict the expected fatigue life of ship structure by analyzing recorded data through a specifically developed post-processing tool.

Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review

This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.