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ESN load forecasting model has high stability, and is able to learn fast and not easy to fall into local optimum, compared with standard recurrent neural network. In the process of constructing the typical ESN model, the choice of parameters is always empirical or random. The forecasting performance of ESN was analyzed on the basis of its key parameters. While the dynamic reserve pool has black box...
According to privatization and deregulation of power system, accurate electric load forecasting has come into prominence recently. The new energy market and the smart grid paradigm ask for both better demand side management policies and for more reliable forecasts from single end-users, up to system scale. However, it is complex to predict the electric demand owing to the influencing factors such...
With the increase in the availability of information regarding energy use, there is an increase of forecasting software on the energy market, that can forecast on the short, medium and long term. In the paper, there is presented a software solution for load forecasting using Artificial Neural Networks (ANN) method. In the case study, we have written an application for a consumer which is engaged in...
Accurate electricity demand forecasts are critical for daily operations planning. They influence many decisions, including commits to produce electricity for a given period. This paper presents a short term electricity demand forecasting system using the Artificial Neural Networks (ANNs). The model is trained and tested on 30-minutes historical load data with temperature from the Electricity Generating...
This paper proposes a novel short-term load forecasting (STLF) method based on extreme learning machine (ELM) and improved gravitational search algorithm (IGSA). The IGSA is used to search the optimal set of input weights and hidden biases for the ELM, improving the basic gravitational search algorithm (GSA) by involving the ability of exploitation in particle swarm optimization (PSO). Based on the...
Short-term load in power system is nonlinear and non-stationary. To cope with the problem that the training error of neural network prediction model is increased while the generalization ability is reduced caused by the large input fluctuation, the rough neurons with upper and lower inputs are introduced into the radial basis function (RBF) neural networks, a power system short-term load forecasting...
Short-term load forecasting is the basis of power system regulation, and it affects many decisions of power system. In order to deal with the challenge of decline in prediction accuracy caused by reduction of cost, and improve the forecasting accuracy and speed, an improved extreme learning machine algorithm, which combines prior knowledge of residential electricity consumption habits is proposed...
The global demand for energy is increasing daily with the expansion of energy infrastructure and the addition of new appliances. Efficient Energy Management System (EMS) is the need of the day. All residential and commercial buildings can achieve better energy efficiency and consumption with the use of EMS. Load forecasting is one of the methods to enable EMS to work efficiently. The accuracy of load...
Precise prediction of load demand is necessary in power systems which can be used in electricity industry and market. Most of load forecasting methods are based on Artificial Neural Networks (ANNs). However ANN parameters need to be adjusted to have an accurate forecasting. For this purpose evolutionary algorithms have been used to optimize the ANN based forecasting. The main obstacle in the use of...
A hybrid algorithm for short-term load forecasting is proposed. The particle swarm optimization algorithm used in the training phase of the artificial neural network is optimized by combining it with the gravitational search algorithm. In this paper, we have combined the exploitation of PSO and exploration of GSA to form a single algorithm that can be used to get more accurate results for load forecast...
The monthly load curve forecasting problem is discussed, being tackled using artificial neural networks (ANN). Authors are proposing an enhanced algorithm that includes nonlinear optimization techniques, such as the conjugate gradient. Thus, a software-tool has been developed. Case study refers to a real distribution network operator from the Western part of Romanian Power System.
The hour ahead load forecasting is used for the reliable and proactive operation of the power system. The hour ahead load forecasting is a one type of Short Term Load Forecasting (STLF). The mostly STLF is used for the spinning reserve capacity, unit commitment and maintenance planning in the power system. In this paper the Linear Regression (LR) and the Artificial Neural Network (ANN) are used to...
A short-term load curve forecasting method based on neural network models was created by means of a neural network tool box in a two step concept: For selection of appropriate training sets of comparable daily demand patterns typical load profiles for different day-types are classified by Kohonen network. The weather-load-correlation is modelled by a multilayer feed-forward-perceptron. To enlarge...
With the recent developments in energy sector, the pricing of electricity is now governed by the spot market where a variety of market mechanisms are effective. After the new legislation of market liberalization in Turkey, competition-based on hourly price has received a growing interest in the energy market, which necessitated generators and electric utility companies to add new dimensions to their...
This paper presents a new approach for shortterm load forecasting using the participatory learning paradigm. Participatory learning paradigm is a new training procedure that follows the human learning mechanism adopting an acceptance mechanism to determine which observation is used based upon its compatibility with the current beliefs. Here, participatory learning is used to train a class of hybrid...
Constant tariff scheme produces a large and continuously-changing difference between electricity cost and price. Consequently, the concern of power system planning and economic generation becomes significant. To overcome this problem accurate load forecasting is a field of immense importance. Conventional methods, i.e., Moving Average (MA) and Holt-Winter (HW) methods are inappropriate to forecast...
Accurate and robust load forecasting models play an important role in power system planning. Due to smaller size and inherent property of good classification, Radial Basis Function Neural Network (RBFNN) is always preferred over other neural network structures. It is used by researchers as an effective tool for Short-Term Load Forecasting (STLF). The smaller size of this network may lead its output...
This paper firstly analyzed the traditional short-term power load forecasting theory and methods and made a detailed research and analysis on the application of BP neural network in short-term power load forecasting, pointing out its deficiencies. Then the refined research was conducted on the prediction model in this paper and eventually the short-term power load forecasting model based on improved...
A Smart Grid approach to electric distribution system management needs to front uncertainties in generation and demand thus making forecasting an up-to-date area of research in electric energy systems. This works aims to propose a day-ahead load forecasting procedure for a medium voltage customer. The load forecasting is performed through the implementation of an artificial neural network (ANN). The...
In this paper, a computational intelligent technique genetic algorithm (GA) is implemented for the optimization of artificial neural network (ANN) architecture. The network structures are normally selected on the basis of the developer's prior knowledge or hit and trial approach is used for this purpose. ANN based models are frequently used for the prediction of future load, because of their learning...
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