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This paper uses multiple regression analysis method to establish customer' s load regression models, which consider economic indicators, air temperature and rainfall. Furthermore, the proposed models are used to study the forecasting feasibility of the future energy sales and summer peak load demand. The least-squares technique is applied to derive regression models of 34 customer energy sales and...
Forecasting of power consumption and planning of the balances of electric power may be said to be the main objective of management of EPS. The amount of energy consumption defines the structure of generating equipment, electric networks configuration, the production of electric and thermal energy, use of energy resources, reliability of power supply, the quality of electric power, and also plays an...
Strong correlation exists between river discharge and suspended sediment load. The relationship was used to estimate suspended sediment load by using linear regression model, power regression model, artificial neural network and support vector machine in this study. Records of river discharges and suspended sediment loads in Kaoping river basin were investigated as case study. Eighty-five percent...
Load forecasting is important for power systems planning, and, on the operational level, for the grid operators and the balance responsible parties. A decrease in the load forecasting error increases the security of supply, and leads to the decrease in the financial costs for both market participants and the power system as a whole. Different studies have investigated influence of load forecasting...
Support vector regression (SVR) has revealed the strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, these employed evolutionary algorithms themselves also have drawbacks, such as premature convergence, slowly reaching the global optimal solution,...
Electricity demand forecasting is known as one of the most important challenges in managing supply and demand of electricity and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ Adaptive...
Based on the GM(1, 1) theory, this paper studies the deficiency in power load forecasting. combining the regression model, the paper proprose a new method to forecast the power load. In this paper, the grey model GM(1, 1) is hybridized into the regression model. This results in grey regression model, which is explained detailedly in succession. Based on an example, the basic grey model and grey regression...
This paper presents an approach to performance modeling of servers managing radio networks, applied in Motorola Government & Public Safety Department. The primary goal of creating the performance model for mission-critical systems is to verify if strict requirements are met even under the highest possible load. By determining capacity of various hardware configurations, the model serves also as...
Electrical load forecasting is one of the important concerns of power systems and has been studied from different views. Electrical load forecast might be performed over different time intervals of short, medium and long term. Various techniques have been proposed for short term, medium term or long term load forecasting. In this study we employ artificial neural networks (ANN) and regression (linear...
The load forecasting of the electric power has been studied extensively in the world. In many cases, the load forecast of the electric power systems is studied in the electric power company rather than demand side. The load forecast in the demand side is affected by some specific factors that are not measured and expected in advance. Although the factors/parameters are limited, it was difficult to...
Based on data collected previously on the electricity market of the East China, we use stepwise regression method to find the approximate expression of the active power flow of each power sets on East Chinapsilas certain electrical network. Then classified discussion is carried out according to the difference of the capacity in and out of merit in order to obtain a simple and reasonable rule for calculating...
With the deterioration of primary energy market supply, it is important to optimize the raw material buying and dispatching. The annual electric power consumption is one of the most important decision making basis to realize this. Because of the characters of observations, OLS method and neural network model are all not suit for this. PLS extract variables one by one from few historical data. Under...
A gray model and regression model based middle and long term load forecasting method using variable weight combination model is proposed. In view of the shortcomings of grey prediction model is not very suitable for middle and long term load forecasting, the equivalent dimensions additional data processing technology is adopted to build the equivalent dimensions additional grey model to improve the...
Short-term load forecasting (STLF) has always been a very important issue in power system planning and operation. Recently, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. Electricity load forecasting is complex to conduct due to its nonlinearity of influenced factors. Support vector machine (SVM) is a novel type of learning...
A new idea is proposed that preprocessing is the key to improving the precision of short-term load forecasting (STLF). This paper presents a new model of STLF which is based on pattern-base. Our model can be described as follows: firstly, it recognizes the different patterns of daily load according such features as weather and date type by means of data mining technology of classification and regression...
Long-term load forecasting has a vital role in generation, transmission and distribution network planning. Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. The purpose of this paper is to introduce two approaches based regression method and artificial neural network...
Analysis of workload execution and identification of software and hardware performance barriers provide critical engineering benefits; these include guidance on software optimization, hardware design tradeoffs, configuration tuning, and comparative assessments for platform selection. This paper uses Model trees to build statistical regression models for the SPEC1 CPU2006 and the SPEC OMP2001 suites...
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