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With the deepening reform to power industries, power systems are going to gradually open the demand side and the power consumers have to face the changes of their role in the market, and make a preliminary long-term forecasting for the electricity prices. In order to consider the relations between the spot market and the long-term contract market, this paper builds a multi-electricity price grey model...
Electric vehicles (EV) do not emit tailpipe exhaust fumes in the same manner as internal combustion engine vehicles. Optimal benefits can only be achieved, if EVS are deployed effectively, so that the tailpipe emissions are not substituted by additional emissions in the electricity sector. This paper examines the potential contributions that Plug in Hybrid Electric Vehicles can make in reducing carbon...
The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques to solve different engineering problems. Besides, Short Term Electrical Load Forecasting (STLF) is one of the important concerns of power systems and accurate load forecasting is vital for managing supply and demand of electricity. This study estimates short term electricity loads...
Accurate electricity demand forecasting is the foundation of power system operation and planning, the basic of placing development plans, business strategy and tactics of the power companies. Electricity consumption is a gray system which is impacted by economic development, industrial structure, income levels and national policies. The paper counted Inner Mongolia electricity data from four factors,...
To settle the problem which the precision and generalization performance of forecast model is affected easily by input variable, the method which reconstructs the original input space of back-propagation neural network by principal component analysis that can eliminate the relevance of value is researched. The method can not only reduce duplicated information but also extract the leading factors....
Power planning engineers are trained to design an electric system that satisfies predicted electrical demand under stringent conditions of availability and power quality. Like responsible custodians, we plan for the provision of electrical sustenance and shelter to those in whose care regulators have given us the responsibility to serve. Though most customers accept this nurturing gladly, a growing...
In this paper, an original technique to explore the long term load dynamics using a multi-scale analysis of the daily peak load based on the empirical mode decomposition (EMD) is presented. The signal is decomposed into intrinsic oscillatory components called intrinsic mode functions (IMFs). These modes are derived from the signal itself and not on a specific basis function. In this work, the EMD...
With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of...
Air conditioner electric load was the most significant factor for residential electrical peak load. Outdoor environmental parameters such as outdoor air temperature and humidity might have effect on air conditioner electric load. A field testing was carried out in Wuhan to study the relationship among residential electric load, outdoor air temperature and humidity. The result showed that the changing...
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 ability to accurately forecast the load plays an important role in electric power system planning and operating. In this paper, a novel approach was proposed for the electricity load forecasting by applying the manifold regularization learning methodology. Unlike traditional methods for load forecasting, the prediction method based on manifold regularization allows us to effectively exploit the...
Present society demands electricity as its basic need in every moment. Future electric energy demand data is an essential requirement for the expansion analysis of all sectors of a power system. This paper presents a logical assessment of the requirement of electric energy for Bangladesh in coming 15 years with a view to provide guideline to the power system expansion planners and countrypsilas energy...
The city is playing an important role in the social economic development. The research on influencing factors, formation mechanisms and problems in the development of cities, especially the relationship between urban electric power and economy growth, is significant to make urban development strategy and complete the urban electric power planning. In this paper, based on the maximum entropy method,...
An improved BP Neural Network with additional momentum and adaptive learning is proposed in the paper to predict the growth rate of electricity consumption in China. Matlab7 is used as modeling tool to design the model. Current year GDP growth, electric power consumption growth and growth rate of secondary industry are taken as input variables while next year electric power consumption growth is predicted...
With the development of power markets, forecasting is becoming more and more important in such new competitive markets since the electricity demand forecasting is the basis of decision making for participants in electricity market. The aim of this project is to develop an electricity demand predictor. In this paper, we present an Grey-based prediction algorithm to forecast a long-term electric power...
This paper summarises some of the main impacts of large amounts of wind power installed in the island of Ireland. Using results from various studies performed on this system, it is shown that wind power will impact on all time frames, from seconds to daily planning of the system operation. Results from studies examining operation of the system with up to approximately 40% of electricity provided by...
Mid-term load forecasting is taken into account as one of the most important policies in the electricity market and brings about many financial, commercial and, even, political benefits. In this paper, artificial neural networks are represented for mid-term load forecasting of Iran national power system. To do so, the multi layer perceptron (MLP) neural network as well as radial basis function (RBF)...
Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-cost...
System marginal price is the unified price of reflecting the short-term supply and demand relation of electric commodity in the electricity market. Confirming the system marginal price is the lever and core content of electricity market. At present, how to predict the system marginal price efficiently is one of the focus problems in the research of electricity market application. In the electricity...
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