Long-term load forecasting in electricity market

Daneshi, H.; Shahidehpour, M.; Choobbari, A.L.

doi:10.1109/eit.2008.4554335

Cited by 47 publications

(27 citation statements)

References 12 publications

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“…Referring Tab. 4, MAPE with proposed technique is less than or equal to 4.63 % for three consecutive years, which is below the normal accepted forecasting MAPE [15,26,33] in case of medium and long term load forecasting. Table 4 shows that MAPE in the predicted data is least in case of proposed ANN architecture, and in MA method on considering more terms of the historical data the prediction goes smoother, which does not provide good forecasting.…”

Section: Observations During Simulationmentioning

confidence: 99%

“…In the literature it is illustrated that there is no exact procedure to select the number of hidden layer and number of neurons in hidden layer. However, some of the researchers have proposed some thumb rules for selecting the number of neurons in the hidden layer [15]. The paper discusses a new methodology of selecting number of neurons in hidden layer of FFNN.…”

Section: Introductionmentioning

confidence: 99%

“…In last twenty years a lot of research is put on the platform of electric load forecasting. Daneshi et al [15] introduced electric load and corresponding load factor model and stated different types of parameters influencing the load i.e. weather, time, population, econometric and demographic data, energy supply and price, regional development, facilities, sales of electrical appliances and some other random disturbances.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Singh¹,

Singh²,

Tripathy³

2012

Journal of Electrical Engineering

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For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

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Section: Observations During Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Singh¹,

Singh²,

Tripathy³

2012

Journal of Electrical Engineering

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“…Traditional studies for long-term load forecasting were based on regression method, which could not provide a true representation of power system behavior in a volatile electricity market. Many studies present traditional methods like neural networks, genetic algorithms, fuzzy rules, which support vector machines, wavelet networks and expert systems [2] [3], while [4]- [6] introduce two approaches based on regression method and artificial neural network (ANN) for long-term load forecast by applying fuzzy sets to ANN for modeling long-term uncertainties and compare the enhanced forecasting results with those of traditional methods. There are also some researchers present artificial neural network (ANN) combined with linear regression [7].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Arfoa¹

2015

EPE

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Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma'an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.

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“…They are normally used to supply the electric utility companies with information to make investments and take decisions regarding planning (equipment purchases), maintenance (staff hiring) and expansion. An example of a long-term load forecasting is described in (Daneshi et al, 2008). An ANN together with fuzzy logic elaborate their prediction in a volatile electricity market based on the forecast growth of population, monthly temperature of the previous year 90 3.…”

Section: Artificial Neural Network In Electrical Applicationsmentioning

confidence: 99%

Artificial recurrent neural networks for the distributed control of electrical grids with photovoltaic electricity

Avila¹

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Realizando el acto de defensa y lectura de la Tesis en la E.T.S.I.T., Madrid, el día de de 2016.Acuerdan otorgar la calificación de:Y, para que conste, se extiende firmada por los componentes del tribunal, la presente diligencia ResumenEl sistema eléctrico actual no ha evolucionado desde sus orígenes. Esto ha hecho que emerjan diferentes problemas los cuales son necesarios afrontar para incrementar el rendimiento de las redes eléctricas. Uno de estos problemas es el crecimiento de la demanda, mientras que otros, como las Tecnologías de la Información y las Comunicaciones (TIC) o la Generación Distribuida (GD), se han desarrollado dentro de la red eléctrica recientemente sin ser propiamente integradas dentro de ella. Esta Tesis afronta los problemas derivados del manejo y la operación de las redes eléctricas existentes y su evolución hacia lo que se considera la red eléctrica del futuro o Smart Grid (SG).El SG nace de la convergencia de cinco aspectos: i) la red eléctrica, ii) TICs, iii) energías renovables, iv) almacenamiento eléctrico y v) Gestión de la Demanda Eléctrica (GDE). Esta Tesis consiste en un primer paso hacia el SG uniendo e integrando los cinco aspectos claves para su desarrollo y despliegue en el futuro cercano. Para ello, la mejora del estado de la red eléctrica se consigue a través del suavizado del consumo agregado. Para lograr este objetivo, se propone el uso de un algoritmo que procese la información proveniente de las TICs para que todas las partes de la red eléctrica se puedan beneficiar. Algunos de estos beneficios son: mejor uso de las infraestructuras, reducción de su tamaño, mayor eficiencia, reducción de costes e integración de GD, entre otros. El algoritmo propuesto está basado en una aproximación distribuida en la que los usuarios son hechos partícipes de sus decisiones, siendo capaces de manejar sus flujos de potencia con este objetivo. El algoritmo se ha implementado siguiendo una estrategia basada en la GDE combinada con el control automático de la demanda que ayude a integrar los Recursos Energéticos Distribuidos (RED) (energías renovables y sistemas de almacenamiento eléctrico), que lo conducen hacia un concepto innovador denominado Gestión de la Activa de la Demanda Eléctrica (GADE).En esta Tesis, una aproximación basada en la Inteligencia Artificial (IA) ha sido utilizada para implementar el algoritmo propuesto. Este algoritmo ha sido construido utilizando Redes Neuronales Artificiales (RNAs), más concretamente Redes Neuronales Recurrentes (RNRs). El uso de RNAs ha sido motivado por las ventajas de trabajar con sistemas distribuidos, adaptativos y no lineales. Y la elección de las RNRs se ha basado en sus propiedades dinámicas, las cuales encajan perfectamente con el comportamiento dinámico no lineal de la red eléctrica. Además, un controlador neural es utilizado para manejar cada elemento de la red eléctrica, incrementado la eficiencia global a través del suavizado del consumo agregado y maximizando el autoconsumo de los RED disponibles. Sin embargo, no existe ningún tipo de...

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Long-term load forecasting in electricity market

Cited by 47 publications

References 12 publications

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Long-Term Load Forecasting of Southern Governorates of Jordan Distribution Electric System

Artificial recurrent neural networks for the distributed control of electrical grids with photovoltaic electricity

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