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Short Term Load Forecasting Using Semi-Parametric Method and Support Vector MachinesJordaan, JA, Ukil, A 23 September 2009 (has links)
Accurate short term load forecasting plays a very
important role in power system management. As electrical load
data is highly non-linear in nature, in the proposed approach,
we first separate out the linear and the non-linear parts, and
then forecast the load using the non-linear part only. The Semiparametric
spectral estimation method is used to decompose a
load data signal into a harmonic linear signal model and a nonlinear
trend. A support vector machine is then used to predict
the non-linear trend. The final predicted signal is then found by
adding the support vector machine predicted trend and the linear
signal part. With careful determination of the linear component,
the performance of the proposed method seems to be more
robust than using only the raw load data, and in many cases
the predicted signal of the proposed method is more accurate
when we have only a small training set.
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Application of Optimal Approach in Load Forecasting and Unit Commitment ProblemsLiao, Gwo-Ching 25 October 2005 (has links)
An Integrated Chaos Search Genetic Algorithm (CGA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper. A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. This thesis presents a hybrid Chaos Search Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment problems (UC). The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined IA and GA, then added chaos search and fuzzy system approach in it. Then we used the hybrid system to solve UC. Numerical simulations were carried out using four cases; ten, twenty and thirty thermal units power systems over a 24-hour period.
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PERFORMANCE EVALUATION OF NEW AND ADVANCED NEURAL NETWORKS FOR SHORT TERM LOAD FORECASTING: CASE STUDIES FOR MARITIMES AND ONTARIOMehmood, Syed Talha 02 April 2014 (has links)
Electric power systems are huge real time energy distribution networks where accurate short term load forecasting (STLF) plays an essential role. This thesis is an effort to comprehensively investigate new and advanced neural network (NN) architectures to perform STLF. Two hybrid and two 3-layered NN architectures are introduced. Each network is individually tested to generate weekday and weekend forecasts using data from three jurisdictions of Canada.
Overall findings suggest that 3-layered cascaded NN have outperformed almost all others for weekday forecasts. For weekend forecasts 3-layered feed forward NN produced most accurate results. Recurrent and hybrid networks performed well during peak hours but due to occurrence of constant high error spikes were not able to achieve high accuracy.
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Hybrid Data Mining and MSVM for Short Term Load ForecastingYang, Ren-fu 21 June 2010 (has links)
The accuracy of load forecast has a significant impact for power companies on executing the plan of power development, reducing operating costs and providing reliable power to the client. Short-term load forecasting is to forecast load demand for the duration of one hour or less. This study presents a new approach to process load forecasting. A Support Vector Machine (SVM) was used for the initial load estimation. Particle Swarm Optimization (PSO) was then adopted to search for optimal parameters for the SVM. In doing the load forecast, training data is the most important factor to affect the calculation time. Using more data for model training should provide a better forecast results, but it needs more computing time and is less efficient. Applications of data mining can provide means to reduce the data requirement and the computing time. The proposed Modified Support Vector Machines approach can be proved to provide a more accurate load forecasting.
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MULTISTEP FRAMEWORK FOR SHORT-TERM LOAD FORECASTING USING MACHINE LEARNING ALGORITHMSilwal, Hari 01 May 2018 (has links)
Traditional forecasting approaches forecast the total system load directly without considering the individual consumer's load. With the introduction of the smart grid, lots of renewable energy resources such as wind and solar are added to the system from consumer side fluctuates the system load and makes forecasting more complex. Thus, it is necessary to forecast individual consumers load. Here, a framework is presented in which individual customer loads is forecasted rather than the system load. At first, a hierarchical cluster analysis is performed to classify daily load patterns into different groups for all the individuals. Then an association analysis is performed to determine critical influential factors that affect the load curve for given day. The next step is the application of a decision tree to establish classification rules between the different groups of the load curve and the critical influential factors. Then, appropriate forecasting models are chosen for different load patterns and the individual load is forecasted. Finally, the forecasted total system load is obtained through an aggregation of an individual load forecasting results. The relative error of forecasting the system load using this framework is compared with the relative errors using SVM regression and this framework had better accuracy. This framework is also used for forecasting the power output of the renewable generation. Also, the results of the day ahead forecast of system load and renewable generation is used for economic power scheduling for the microgrid and peak shaving for the utilities.
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Prediction of Electricity Price Quotation Data of Prioritized Clean Energy Power Generation of Power Plants in The Buyer's MarketLi, Jiasen 05 October 2021 (has links)
No description available.
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A generalized ANN-based model for short-term load forecastingDrezga, Irislav 06 June 2008 (has links)
Short-term load forecasting (STLF) deals with forecasting of hourly system demand with a lead time ranging from one hour to 168 hours. The basic objective of the STLF is to provide for economic, reliable and secure operation of the power system.
This dissertation establishes a new approach to artificial neural network (ANN) based STLF. It first decomposes the prediction problem into representation and function approximation problems. The representation problem is solved using phase-space embedding which identifies time delay variables from load time series that are used in forecasting. The concept is inherently different from the methods used so far because it does not use correlated variables for forecasting. Temperature variables are included as well using identified embedding parameters. Function approximation problem is approached using ANN ensemble and active selection of a training set. Training set is selected based on predicted weather parameters for a prediction horizon. Selection is done applying the k-nearest neighbors technique in a temperature-based vector space. A novel approach of pilot set simulation is used to determine the number of hidden units for every forecast period. Ensemble consists of two ANNs which are trained and cross validated on complementary training sets. Final prediction is obtained by a simple average of two trained ANNs.
The described technique is used for predicting one week’s load in four selected months in summer peaking and winter peaking US utilities. Mean absolute percent errors (MAPEs) for 24-hour lead time predictions are slightly greater than 2% for all months. For 120-hour lead time (weekday) predictions, MAPEs are around 2.3%. MAPEs for 48- hour lead time (weekend) predictions are around 2.5%. Maximal errors for these cases are around 7%. Predictions for one-hour lead time are slightly higher than 1% for all months, with maximal errors not exceeding 4.99%. Peak load MAPEs are 2.3% for both utilities. Maximal peak-load errors do not exceed 6%. The technique shows very good performance faced with sudden and large changes in weather. For changes in temperature larger than 20° F for two consecutive days, forecasting error is smaller than 3.58%. / Ph. D.
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Short term load forecasting using neural networksNigrini, L.B., Jordaan, G.D. January 2013 (has links)
Published Article / Several forecasting models are available for research in predicting the shape of electric load curves. The development of Artificial Intelligence (AI), especially Artificial Neural Networks (ANN), can be applied to model short term load forecasting. Because of their input-output mapping ability, ANN's are well-suited for load forecasting applications.
ANN's have been used extensively as time series predictors; these can include feed-forward networks that make use of a sliding window over the input data sequence. Using a combination of a time series and a neural network prediction method, the past events of the load data can be explored and used to train a neural network to predict the next load point.
In this study, an investigation into the use of ANN's for short term load forecasting for Bloemfontein, Free State has been conducted with the MATLAB Neural Network Toolbox where ANN capabilities in load forecasting, with the use of only load history as input values, are demonstrated.
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[en] A SHORT-TERM LOAD FORECASTING MODEL USING NEURAL NETWORK AND FUZZY LOGIC. / [pt] MODELO DE PREVISÃO DE CARGA DE CURTO PRAZO UTILIZANDO REDES NEURAIS E LÓGICA FUZZYFLAVIA CRISTINA DA COSTA SERRAO 22 May 2003 (has links)
[pt] O objetivo principal desta dissertação é desenvolver um
método de previsão de carga elétrica de curto prazo
(previsão horária), através de um sistema híbrido
(Redes Neurais e Lógica Fuzzy) utilizando temperaturas
máximas e mínimas como variáveis explicativas. Como
primeiro passo, foram definidos os perfis homogêneos das
curvas de carga diárias através de um classificador
utilizando os Mapas Auto Organizáveis (Self-Organizing Maps-
SOM). Um previsor será adicionado ao esquema de previsão
através da Lógica Fuzzy que associará as variáveis
climáticas aos perfis criados pela SOM produzindo as
previsões.
O modelo foi aplicado em dados de duas concessionárias de
energia elétrica do Brasil usando dados horários coletados
durante dois anos. / [en] This dissertation presents a short-term load forecasting
procedure mixing a classifier scheme and a predictive
scheme. The classifier is implemented through
an artificial neural network using a non-supervised
learning procedure (SOM). Concerning the predictive scheme,
a fuzzy logic procedure uses climatic variables and their
prediction to choose the appropriate profiles created by
SOM and then combines them to produce the desired forecast.
The model is applied to two utilities in Brazil using
hourly observations collected during two calendar years and
the results obtained, in terms of mean absolute percentage
error (MAPE) through the period analyzed, are presented.
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Um estudo sobre os métodos de amortecimento exponencial para a previsão de carga a curto prazoPedreira, Taís de Medeiros 05 September 2018 (has links)
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Previous issue date: 2018-09-05 / As previsões a curto prazo da carga elétrica (de algumas horas até alguns dias à frente) são essenciais para o planejamento, controle e operação dos sistemas de energia, tanto por por razões técnicas quanto financeiras. Como não é possível estocar grandes quantidades, torna-se indispensável um maneira eficaz de programar a produção da energia para que ela atenda a demanda. Por conta disso, uma grande literatura desenvolveu-se sobre o assunto. Devido à complexidade das séries de carga e à dependência não-linear destas carga em relação a diversas variáveis exógenas, os sistemas de previsão mais frequentemente propostos em trabalhos recentes são aqueles baseados em algoritmos complexos de inteligência computacional. No entanto, métodos lineares simples ainda são muito comumente usados, por si sós ou em combinação com técnicas não-lineares. Um desses métodos é o de Holt-Winters-Taylor, que é uma adaptação do conhecido método de amortecimento exponencial de Holt-Winters para que múltiplas sazonalidades possam ser modelados concomitantemente. Este trabalho implementa três variantes deste método HWT e analisa seus desempenhos em duas séries de dados reais de carga. Verificou-se que uma combinação linear dessas variantes nitidamente supera o método HWT original e fornece previsões precisas, com um baixo custo computacional. / Short-term load forecasts (forecasts for horizons ranging from a few hours to a few days ahead) are essential for the planning, controling and operation of energy systems, both for technical and financial reasons. Since it is not feasible to store energy in large quantities, an efficient way to forecast energy demand becomes indispensable. Because of this, a large literature has developed on the subject. Due to the complexity of load series and the nonlinear relationship of the load with exogenous variables, the most frequently proposed forecasting systems in recent papers are those based on complex algorithms of computational intelligence. However, simple linear methods are still very frequently used, either alone or in combination with non-linear techniques. One of these methods is Holt-Winters-Taylor (HWT), which is an adaptation of the well-known Holt-Winters exponential smoothing method, modified so that multiple seasonalities can be modeled at the same time. In this paper, we implement three variants of this HWT method and analyze their performances over two sets of actual load data. We found that a linear combination of these variants clearly outperforms the original HWT method, and provides accurate forecasts at a low computational cost.
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