A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

Lungu, Ion; Căruţaşu, George; Pîrjan, Alexandru; Oprea, Simona-Vasilica; Bârã, Adela

doi:10.24846/v25i1y201609

Cited by 8 publications

(15 citation statements)

References 14 publications

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“…The LM training algorithm represents a merger of two optimization techniques: the gradient descent and the Gauss-Newton methods, taking advantages of both these two component parts. Thus, when the obtained forecasting accuracy is low, the algorithm behaves like the gradient descent method in order to obtain the final convergence, while in the case when the forecasted results are close to the experimental ones, the algorithm performs like the Gauss-Newton method [31][32][33][34]. In this way, the LM training algorithm provides a suite of irrefutable advantages to the scientists and therefore it has been chosen to be implemented in this research, in view of developing the NARX ANN forecasting solution for the month-ahead daily consumed electricity, using the timestamps dataset as exogenous variables.…”

Section: The Non-linear Autoregressive With Exogenous Inputs (Narx) Mmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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An accurate forecast of the electricity consumption is particularly important to both consumers and system operators. The purpose of this study is to develop a forecasting method that provides such an accurate forecast of the month-ahead hourly electricity consumption in the case of medium industrial consumers, therefore assuring an intelligent energy management and an efficient economic scheduling of their resources, having the possibility to negotiate in advance appropriate billing tariffs relying on accurate hourly forecasts, in the same time facilitating an optimal energy management for the dispatch operator. The forecasting method consists of developing first non-linear autoregressive, with exogenous inputs (NARX) artificial neural networks (ANNs) in order to forecast an initial daily electricity consumption, a forecast that is being further processed with custom developed long short-term memory (LSTM) neural networks with exogenous variables support in order to refine the daily forecast as to achieve an accurate hourly forecasted consumed electricity for the whole month-ahead. The obtained experimental results (highlighted also through a very good value of 0.0244 for the root mean square error performance metric, obtained when forecasting the month-ahead hourly electricity consumption and comparing it with the real consumption), the validation of the developed forecasting method, the comparison of the method with other forecasting approaches from the scientific literature substantiate the fact that the proposed approach manages to fill a gap in the current body of knowledge consisting of the need of a high-accuracy forecasting method for the month-ahead hourly electricity consumption in the case of medium industrial consumers. The developed forecasting method targets medium industrial consumers, but, due to its accuracy, it can also be a useful tool for promoting innovative business models with regard to industrial consumers willing to produce a part of their own electricity using renewable energy resources, benefiting from reduced production costs and reliable electricity prices.

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Section: The Non-linear Autoregressive With Exogenous Inputs (Narx) Mmentioning

confidence: 99%

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Petroşanu¹

2019

Processes

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“…The main motivation and starting point in devising our research methodology consisted of designing, developing, and implementing a new forecasting method for both the produced and consumed electricity of small wind farms situated on quite complex hilly terrain that offers an improved accuracy when compared to the forecasting method that some members of our research team previously developed and implemented in a series of wind farms [25].…”

Section: Methodsmentioning

confidence: 99%

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

2018

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Accurate forecasting of the produced and consumed electricity from wind farms is an essential aspect for wind power plant operators. In this context, our research addresses small-scale wind farms situated on hilly terrain, with the main purpose of overcoming the low accuracy limitations arising from the wind deflection, caused by the quite complex hilly terrain. A specific aspect of our devised forecasting method consists of incorporating advantages of recurrent long short-term memory (LSTM) neural networks, benefiting from their long-term dependencies, learning capabilities, and the advantages of feed-forward function fitting neural networks (FITNETs) that have the ability to map between a dataset of numeric inputs and a set of numeric targets. Another specific element of our approach consists of improving forecasting accuracy by means of refining the accuracy of the weather data input parameters within the same weather forecast resolution area. The developed method has power plant operators as main beneficiaries, but it can also be successfully applied in order to assess the energy potential of hilly areas with deflected wind, being useful for potential investors who want to build this type of wind farms. The method can be compiled and incorporated in the development of a wide range of customized applications targeting electricity forecasting for small wind farms situated on hilly terrain with deflected wind. The experimental results, the implementation of the developed method in a real production environment, its validation, and the comparison between our proposed method and other ones from the literature, confirm that the developed forecasting method represents an accurate, useful, and viable tool that addresses a gap in the current state of knowledge regarding the necessity for an accurate forecasting method that is able to predict with a high degree of accuracy both the produced and consumed electricity for small wind power plants situated on quite complex hilly terrain with deflected wind.

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“…Thus, another problem is that a WT effectively generates energy in a narrow band of wind speed, whereas wind speed is very volatile even over small areas. Both maintaining grid stability and increasing WF effectiveness are a challenge for wind power improvement [2,3,8].…”

Section: Problems Of Wind Energymentioning

confidence: 99%

“…Obviously, such approaches and similar ones require big investments for mass production. So, another optimistic way is to cover the wind statistics with multiple WTs, each of which holds its narrow band of wind speeds [8,3]. In this way, a WF is projected and built without construction inventions but just with allocating and using WTs optimally.…”

Section: Background and Motivationmentioning

confidence: 99%

Wind Farm Energy and Costs Optimization Algorithm under Uncertain Parameters of Wind Speed Distribution

Romanuke¹

2019

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A Two-step Forecasting Solution and Upscaling Technique for Small Size Wind Farms Located in Hilly Areas of Romania

Cited by 8 publications

References 14 publications

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Designing, Developing and Validating a Forecasting Method for the Month Ahead Hourly Electricity Consumption in the Case of Medium Industrial Consumers

Designing, Developing, and Implementing a Forecasting Method for the Produced and Consumed Electricity in the Case of Small Wind Farms Situated on Quite Complex Hilly Terrain

Wind Farm Energy and Costs Optimization Algorithm under Uncertain Parameters of Wind Speed Distribution

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