Up to year 2020 load forecasting using neural nets

Kermanshahi, B.; Iwamiya, H

doi:10.1016/s0142-0615(01)00086-2

Cited by 147 publications

(57 citation statements)

References 16 publications

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“…The reason for using such data is to provide the most relevant information to the network and then let the network do pattern matching among the inputs and outputs. Sometimes, not only the straight (actual) data are provided, but also the differences between the present and previous status of the data is important, which are called incremental rate data (Kermanshahi & Iwamiya, 2002).…”

Section: Fundamentals Of Artificial Neural Networkmentioning

confidence: 99%

“…Selecting the number of neurons and layers is an iterative process at the moment. When the number of hidden neurons is fewer than the required, errors increase and correlation between inputs and outputs becomes weak, and when the number of hidden neurons is more than the required, problem of over learning causes increasing variance in the predictions (Kermanshahi & Iwamiya, 2002).…”

Section: Fundamentals Of Artificial Neural Networkmentioning

confidence: 99%

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Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

115

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New Zealand farmers practice a form of ‗industrialised' agriculture that relies on relatively high inputs of fossil fuels, not only to power machinery directly but also for manufacturing artificial fertilizers and agrichemicals (Wells, 2001). Consequently, New Zealand is one of the countries with the highest energy input per unit weight (in agriculture) in the world (Conforti & Giampietro, 1997). Furthermore, in terms of shipping, the influence of increasing global fuel costs is greater on New Zealand farming than in other countries. The main aim of this study was to estimate energy consumption in wheat production. Energy determination can give a clear picture of farms in order to compare different farming systems and energy inputs. The second main target of this study was to develop a neural network model to simulate and predict energy use in wheat production under different conditions incorporating social, geographical, and technical factors. Additionally, the interaction effects between different factors were examined in this study.This study was conducted on irrigated and dryland wheat fields in Canterbury, New Zealand, in the 2007-2008 harvest year. Canterbury represents 87% of the wheat area and 66% of the arable area harvested in New Zealand.Energy consumption here is defined as the energy used for the production of wheat until it leaves the farm. The data were collected from three different sources:questionnaire, literature review, and field measurements. The energy inputs estimated in this study are those that go into on-farm production systems before the post-harvest processes. The study considered only the energy used in wheat production, without taking into account the natural sources of energy (radiation, wind, rain, etc). IVA survey was conducted to collect the most important data and to identify farmers' attitudes and opinions about energy consumption. In this study, 40 arable farms were selected randomly, as far as possible. From the initial analysis, it was found that 30 farms were irrigated and the rest were dryland farms. Irrigated farms were irrigated between one to ten times annually depending on the rainfall. Some irrigated farms have also been converted to dryland farms, or vice versa, in different years. The data for a large number of farming factors were gathered in the survey.Average energy consumption for wheat production was estimated at around 22,600 MJ/ha. On average, fertilizer and electricity (mostly for irrigation) were used more than other energy sources, at around 10,654 MJ/ha (47%) and 4,870 MJ/ha (22%), respectively. The average energy consumption for wheat production in irrigated farming systems and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively. This study is the first to create an appropriate Artificial Neural Network (ANN) model to predict energy consumption in wheat production with optimum variables. This study would be the first to investigate the factors related to the efficient use of energy in agricultural production. A careful study o...

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Section: Fundamentals Of Artificial Neural Networkmentioning

confidence: 99%

Section: Fundamentals Of Artificial Neural Networkmentioning

confidence: 99%

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Safa

Samarasinghe

2011

Energy

115

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“…To solve the problem of forecasting electric power consumption, traditional statistical methods based on specific norms for electric power consumption and models based on expert systems and artificial neural networks can be used [6][7][8][9].…”

Section: Methodsmentioning

confidence: 99%

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

2017

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Abstract.In article the analysis of electric power consumption and problems of power saving on coal mines are considered. Nowadays the share of conditionally constant costs of electric power for providing safe working conditions underground on coal mines is big. Therefore, the power efficiency of underground coal mining depends on electric power expense of the main technological processes and size of conditionally constant costs. The important direction of increase of power efficiency of coal mining is forecasting of a power consumption and monitoring of electric power expense. One of the main approaches to reducing of electric power costs is increase in accuracy of the enterprise demand in the wholesale electric power market. It is offered to use artificial neural networks to forecasting of day-ahead power consumption with hourly breakdown. At the same time use of neural and indistinct (hybrid) systems on the principles of fuzzy logic, neural networks and genetic algorithms is more preferable. This model allows to do exact short-term forecasts at a small array of input data. A set of the input parameters characterizing mining-and-geological and technological features of the enterprise is offered.

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“…Load forecasting can be divided into short-term, mid-term and long-term forecasting. Short-term, mid-term and long-term load forecasts are range from an hour to one week, one week to one year and one year to decades, respectively [2,3]. For short-term load forecasting (STLF) several factors should be considered, especially such as time, weather and renewable sources.…”

Section: Introductionmentioning

confidence: 99%

“…The medium-term and LTLF take into account the historical load, weather, the number of customers in different categories and other factors [4]. Many LTLF techniques have been proposed used for resource planning and utility expansion in the last 30 years [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Many software packages have been made for safety and quality of energy systems management.…”

Section: Introductionmentioning

confidence: 99%

Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

Çetinkaya¹

2017

ijisae

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Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and mean absolute error (MAE) are used. The obtained results show that the proposed algorithms are available.

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Up to year 2020 load forecasting using neural nets

Cited by 147 publications

References 16 publications

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

The Increase of Power Efficiency of Underground Coal Mining by the Forecasting of Electric Power Consumption

Improving an Expert-Supported Dynamic Programming Algorithm and Adaptive-Neuro Fuzzy Inference System for Long-Term Load Forecasting

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