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This article aims at developing long term electricity forecasts for Morocco, using Non Linear Autoregressive model (NLARX) based on a special class of Artificial Neural Networks (ANN), namely Wavelet Networks. This model provides more accurate forecasting results thanks to the nature of the used neural networks structure that has been developed in other fields. The obtained results will help design...
This paper presents a regression based moving window model for solving the short-term electricity forecasting problem. Moving window approach is employed to trace the demand pattern based on the past history of load and weather data. Regression equation is then formed and least square method is used to determine the parameters of the model. In this paper, a new concept associated with cooling and...
The introduction of demand side Advanced Metering Infrastructures in power distribution grids, allows the collection of huge amount of valuable information about energy usage. Utilities are already exploiting such information through Demand Side Management and Forecasting Algorithms that have been proved to help reducing the overall electricity demand. To push further this “green” trend toward the...
A hybrid mid-term electricity MCP forecasting model combining both support vector machine (SVM) and autoregressive moving average with external input (ARMAX) modules is presented in this paper. Currently, there are many techniques available for short-term electricity market clearing price (MCP) forecasting, but very little has been done in the area of mid-term electricity MCP forecasting. Mid-term...
A non-stationary forecasting model for rice prices is proposed in this paper. A daily rice price has two behaviors i.e. normal and spiky. To encounter both behaviors, non-stationary model has been applied. From various type of non-stationary model, the simple one which contains ARIMA model and GARCH model are applied. The ARIMA model is applied to catch the linear relationship of non-stationary data...
The aim of this research is to develop a forecasting model for half-hourly electricity load in Java-Bali Indonesia by using two-level seasonal model based on hybrid ARIMA-ANFIS. This two-level forecasting model is developed based on the ARIMA model at the first level and ANFIS for the second level. The forecast accuracy is compared to the results of the individual approach of ARIMA and ANFIS. Data...
Private houses are more and more enabled with devices that can produce renewable energy, and the not so remote chance of selling the surplus energy makes them new players in the energy market. This market is likely to become deregulated since each energy home-producer can negotiate the energy price with consumers, typically by means of an auction; on the other hand, consumers can always rely on energy...
Mathematical description and modeling of dynamic systems is challenging due to their high level of complexity, their nonlinear and chaotic behaviors, the presence of uncertainties and interference of human behavior in their outputs, and their time-variant nature. Because of such characteristics and the importance of dynamic systems modeling, high-performance modeling tools are required to analyze,...
Transmission congestion occurs when power flow over a transmission line exceeds its thermal limit causes high fluctuation of locational marginal prices (LMPs). It eventually influences the revenue, incomes, and profits of the market participants. Hence an accurate LMP forecasting is important to ensure economical operation as well as proper scheduling of generation resources. It helps market participants...
Precise electricity demand forecasting is necessary for power sector planning. Electricity consumption is typically governed by variation in severaleconomic and demographic variables including population, GDP and income per capita. In this study, relationship has been investigated between electrical power demand and the selected independent variables through correlation matrix. Univariate time series...
Along with the rapidly developing rural economy and the change of people's consumption concept, rural electricity consumption increased rapidly. The scientific prediction for rural electricity consumption is the decision-making basis and guarantee for plan in the next reconstruction of rural distribution network. There are many common prediction methods, which has its own relative merits and disadvantages...
In this paper, we propose an improved combined forecasting model integrates the merits of data pretreatment, combined model and Markov chain, known as Markov combined model. The moving average is used for the data pretreatment or determination of trend, combined model is designed for the trend forecasting, and the Markov chain is employed for modifying the forecasting results of combined model. The...
Under deregulated environment, accurate electricity price forecasting is a crucial issue concerned by all participants. Experience shows that single forecasting model is very difficult to improve the forecasting accuracy due to the complicated factors affecting electricity prices. A particle swarm optimization (PSO) based GM(1,2) method on day-ahead electricity price forecasting with predicted error...
An essential element of electric utility resource planning is the long term forecast of the electricity consumption. This paper presents an approach to forecast annual electricity consumption by using artificial neural network based on historical data for Malaysia. It involves developing several ANN designs and selecting the best network that can produce the best results in terms of its accuracy....
Nowadays, many researches are made to estimate some of socio-economic variables in which methods such as regression, time series (ARIMA, AR and etc.), Artificial Neural Networks (ANN) and so on are used. In this paper integrated System Approach and ANN are applied for estimating affects of subsidy on electricity consumption and social welfare. Actual electricity price is estimated by ANN, which has...
This paper presents a forecasting technique to predict next-day electricity spot prices and volatilities. Our technique combines a fundamental model formulated as supply stack modeling, with an econometric analysis based on the GARCH methodology. Empirical results from the wholesale electricity market of Great Britain are discussed.
Under deregulated environment, accurate price forecasting provides crucial information for electricity market participants to make reasonable competing strategies. With comprehensive consideration of the changing rules of the day-ahead electricity price of the PJM electricity market, a day-ahead electricity price forecasting method based on particle swarm optimization (PSO) and GM(1, 1) model is proposed,...
Short-term electricity price forecasting in competitive power markets is essential both for producers and consumers in planning their operations and maximizing their benefits. This paper proposed a new grey model, called PGM(1,2), based on Particle Swarm Optimization algorithm (PSO) and correlation hours method (CHM) in order to forecast short-term price in the Nordpool market. The main sequence is...
The traditional gray forecasting model is widely used in various fields, but it has some limitations. In this paper, a method based on genetic algorithm optimizing gray modeling process is introduced, and the flow chart of modeling is given. This method makes full use of the advantages of the gray forecasting model and characteristics of genetic algorithm to find global optimization. So the forecasting...
The price of coal and electricity depends on various indeterminate factors, and there is a very complicated coupling relationship among them. The forecasting model becomes more complex for their strong nonlinear features that bring lots of difficulties in constructing a precise forecasting model with the traditional methods. This paper proposes a new method, on the basis of PSO and RBF neural network,...
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