Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities

Kermanshahi, B.

doi:10.1016/s0925-2312(98)00073-3

Cited by 104 publications

(41 citation statements)

References 6 publications

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“…In recent years, much research has been conducted on the application of artificial intelligence techniques to load forecasting problems [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. However, the models that have received the most extensive attention are undoubtedly the ANNs, cited among the most powerful computational tools ever developed.…”

Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

03/02355 Regional load forecasting in Taiwan - applications of artificial neural networks

2003

Fuel and Energy Abstracts

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Up to now, the general style of load forecasting emphasized aggregate load forecasting. Such load forecasting results not only cannot identify where the power load takes place but also is not helpful for power facilities construction location planning. On the other hand, the power industry has been moving toward a deregulated environment recently. The results of regional load prediction could be used by power retailers to find their potential business opportunities. For transmission and distribution operators, accurate regional load forecasting can help them in long term power system planning and construction. Thus, regional load forecasting is getting more and more important for electricity providers in a deregulated power market. In this paper, empirical data are collected to formulate an artificial neural network model to predict the regional peak load of Taiwan. Based on the forecast results, some suggestions for Taiwan power market providers are presented.

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Section: Artificial Intelligence Based Methodsmentioning

confidence: 99%

03/02355 Regional load forecasting in Taiwan - applications of artificial neural networks

2003

Fuel and Energy Abstracts

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“…In 1987, ref. [28] Before that, Kermanshahi [91] in 1998 used ANN forecast load for 10 years, Ekonomou [92] used ANN to forecast load in Greece. Other commendable work in LTLF using ANN is reported in literatures [93][94][95][96][97][98][99][100].…”

Section: Long-term Load Forecasting Overviewmentioning

confidence: 99%

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Khuntia

Rueda

Meijden

2016

IET Generation, Transmission & Distribution

127

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Load forecasting has always been an important part in the planning and operation of electric utilities, i.e., both transmission and distribution companies. With technological advancement, change in economic condition and many other factors (to be discussed in this work), load forecasting is becoming more important. The forecast affects as well as gets affected because of the load impacting factors and actions taken in different time horizons. However, due to its stochastic and uncertainty characteristics, it has been one challenging problem for electrical utilities to accurately forecast future load demand. This paper aims at reviewing the different load forecasting techniques developed for the mid-and long-term horizons of electrical power systems. Since there has never been an explicit literature study of the various forecasting techniques for mid-and long-term horizons, this paper reviews techniques for each of the forecasting horizons, citing various methodologies developed so far supported by published literature. The study is concluded with discussion on future research directions.

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“…After each output unit provides the information relating to one time-step interval, the training of the network becomes stable. Based on conventional research [21], the authors think that it is convenient to make the forecast model by a trial-and-error approach. As a consequence, the past information is maintained to RNN with the progress of learning.…”

Section: Pv Power Output Forecastingmentioning

confidence: 99%

Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

et al. 2014

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This paper proposes the re-planning operation method using Tabu Search for direct current (DC) smart house with photovoltaic (PV), solar collector (SC), battery and heat pump system. The proposed method is based on solar radiation forecasting using reported weather data, Fuzzy theory and Recurrent Neural Network. Additionally, the re-planning operation method is proposed with consideration of solar radiation forecast error, battery and inverter losses. In this paper, it is assumed that the installation location for DC smart house is Okinawa, which is located in Southwest Japan. The validity of proposed method is confirmed by comparing the simulation results.

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Recurrent neural network for forecasting next 10 years loads of nine Japanese utilities

Cited by 104 publications

References 6 publications

03/02355 Regional load forecasting in Taiwan - applications of artificial neural networks

03/02355 Regional load forecasting in Taiwan - applications of artificial neural networks

Forecasting the load of electrical power systems in mid‐ and long‐term horizons: a review

Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting

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