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Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation...
Electrical load forecasting is of great significance to guarantee the system stability under large disturbances, and optimize the distribution of energy resources in the smart grid. Traditional prediction models, which are mainly based on time series analyzing, have been unable to fully meet the actual needs of the power system, due to their non-negligible prediction errors. To improve the forecasting...
In this paper, an evaluation theory of hybrid model for short-term electricity load forecasting is presented using simple soft-technique of predicting data. A model that integrates fuzzy system with neural network database is demonstrated and eventually compared with a traditional statistical method of linear regression. Power load forecasting errors especially for weekends, which is much higher than...
This paper presents an application of linear mixed models to short-term load forecasting. The starting point of the design is a currently working model at the Spanish Transport System Operator, which is based on linear autoregressive techniques and neural networks. The forecasting system currently forecasts each of the regions within the Spanish grid separately, even though the behavior of the load...
One of the most crucial tasks for utility companies is load forecasting in order to plan future demand for generation capacity and infrastructure. Improving load forecasting accuracy over a short period is a challenging open problem due to the variety of factors that influence the load, and the volume of data that needs to be considered. This paper proposes a new approach for short term load forecasting...
Load forecasting is a data intensive statistical method. Internet of things (IoT) based online load forecasting (LF) collects those data from internet on demand and then performs fast statistical and optimization methods for forecasting efficiently. IoT based online LF not only depends on power systems properties, but also internet, machine-to-machine (M2M) connections, communications and computation...
Improving the accuracy of power system load forecasting is important for economic dispatch. However, a load sequence is highly nonstationary and hence makes accurate forecasting difficult. In this paper, a method based on wavelet decomposition (WD) and a second-order gray neural network combined with an augmented Dickey–Fuller (ADF) test is proposed to improve the accuracy of load forecasting. First,...
Energy Consumption in a neighborhood depends upon its socioeconomic parameters. Demographical diversities in a neighborhood in India warrants load prediction at distribution transformer (DT) rather than at utility level. In this paper two interesting techniques of load forecasting have been proposed which have not be explored till date. In both these technique a unique pattern of consumption has been...
In smart grid, one of the most important research areas is load forecasting; it spans from traditional time series analysis to recent machine learning approach and mostly focuses on forecasting aggregated electricity consumption. However, the importance of demand side energy management including individual load forecasting is becoming critical. In this paper, we propose deep neural network (DNN) based...
Power system load forecasting is the basis of power system planning and operation, in order to improve the accuracy of power system load forecasting and ensure the stable operation of power system, a number of related indices must be taken into consideration in the research of power system load forecasting. The tradition principal component analysis is always used to process these indices. In allusion...
In this paper, the periodicity and variation of power system load data has been analysed with bad data removed when correlation process was conducted, and proper parameter has been applied to be the restraint weight of neuron. Then back propagation (BP) neural network and radial basis function (RBF) neural network has been established by means of MATLAB. The load is predicted by the use of model and...
Load forecast is becoming currently more fundamental in planning, operating and controlling of modern electric power systems. Nowadays the load peak forecast is also important, because it is of great interest in economy stability and improvement in the electrical systems. This paper presents an approach for load forecasting in the medium voltage distribution network in Portugal. The forecast horizon...
In this paper, a new ensemble forecasting model for short-term load forecasting (STLF) is proposed based on extreme learning machine (ELM). Four important improvements are used to support the ELM for increased forecasting performance. First, a novel wavelet-based ensemble scheme is carried out to generate the individual ELM-based forecasters. Second, a hybrid learning algorithm blending ELM and the...
Electricity demand forecasting is a nonlinear and complex problem. It consists of three levels, including long-term forecast for new power plant planning, medium-term forecast for maintenance scheduling and inventory of fuel, and short-term forecast for daily operations. There are many statistical forecasting techniques applied to short term load forecasting, such as Stochastic Time series, Regression...
Short-Term Load and Price forecasting are crucial to the stability of electricity markets and to the profitability of the involved parties. The work presented here makes use of a Local Linear Wavelet Neural Network (LLWNN) trained by a special adaptive version of the Particle Swarm Optimization algorithm and implemented as parallel process in CUDA. Experiments for short term load and price forecasting,...
This paper presents the results for daily load forecasts of a local power supplier. The new approach of this paper is to use three neural networks, each representing a period of a day, for the daily load forecast. Three commercial tools for neural networks are used to reduce the time for the development and tests. The neural networks are tested with real load values of a the power supplier for a period...
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 shows the application of the Neighbor Histories (NH) algorithm to the problem of short term electrical load forecasting in a utility company. This algorithm is a simple application of embedding theorems recently used in chaotic time series prediction. The choice of the parameters of the algorithm is usually done manually by trial and error. In this paper the possibility of automatic selection...
Smart Grids — intelligent electricity grids enable two way communication between utility and its customers. Smart Grid technologies are being deployed throughout the world, to make the traditional grids more reliable, secure and environmentally sustainable. Consumers are the important domain in a Smart Grid, participating in real-time demand response programs. To ensure the qualities of a Smart Grid...
Short term load forecasting is critically important in modern electricity networks since it helps provide supportive information for reliable power system operation in competitive electricity market environment. In this paper, the wavelet analysis based neural network model is employed to forecast the electricity demand in short-term period. The wavelet analysis helps to decompose the electricity...
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