A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption

Kaytez, Fazıl

doi:10.1016/j.energy.2020.117200

Cited by 136 publications

(60 citation statements)

References 40 publications

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“…The average relative generalization error was reduced to 2.86%, and energy utilization efficiency was increased by 6.38% [45]. LSSVM-based hybrid models were developed for forecasting the energy demand of the grid [46] as well as the energy consumption of complex industrial processes to ensure the efficient operation management and control of the cement industry [47]. In other studies, ANN and LSSVM were used for dynamic optimization of a pilot-scale entrained flow gasifier operation [48].…”

Section: Of 33mentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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This paper presents a comprehensive step-wise methodology for implementing industry 4.0 in a functional coal power plant. The overall efficiency of a 660 MWe supercritical coal-fired plant using real operational data is considered in the study. Conventional and advanced AI-based techniques are used to present comprehensive data visualization. Monte-Carlo experimentation on artificial neural network (ANN) and least square support vector machine (LSSVM) process models and interval adjoint significance analysis (IASA) are performed to eliminate insignificant control variables. Effective and validated ANN and LSSVM process models are developed and comprehensively compared. The ANN process model proved to be significantly more effective; especially, in terms of the capacity to be deployed as a robust and reliable AI model for industrial data analysis and decision making. A detailed investigation of efficient power generation is presented under 50%, 75%, and 100% power plant unit load. Up to 7.20%, 6.85%, and 8.60% savings in heat input values are identified at 50%, 75%, and 100% unit load, respectively, without compromising the power plant’s overall thermal efficiency.

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Section: Of 33mentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

et al. 2020

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“…Deep learning models employed to model the waste to heat recovery system have performed well in mapping the system's dynamic response [20]. LSSVM based hybrid models are reported for forecasting the energy demand of the grid [23] and the energy consumption of complex industrial processes to ensure the efficient operation management and control of the cement industry [24].…”

Section: Introductionmentioning

confidence: 99%

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Ashraf

Uddin

Kamal³

et al. 2020

Energies

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Modern data analytics techniques and computationally inexpensive software tools are fueling the commercial applications of data-driven decision making and process optimization strategies for complex industrial operations. In this paper, modern and reliable process modeling techniques, i.e., multiple linear regression (MLR), artificial neural network (ANN), and least square support vector machine (LSSVM), are employed and comprehensively compared as reliable and robust process models for the generator power of a 660 MWe supercritical coal combustion power plant. Based on the external validation test conducted by the unseen operation data, LSSVM has outperformed the MLR and ANN models to predict the power plant’s generator power. Later, the LSSVM model is used for the failure mode recovery and a very successful operation control excellence tool. Moreover, by adjusting the thermo-electric operating parameters, the generator power on an average is increased by 1.74%, 1.80%, and 1.0 at 50% generation capacity, 75% generation capacity, and 100% generation capacity of the power plant, respectively. The process modeling based on process data and data-driven process optimization strategy building for improved process control is an actual realization of industry 4.0 in the industrial applications.

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“…A reliable forecasting model is necessary for accurate investment planning of electricity generation and distribution. Fazil Kaytez [19] proposed a hybrid model based on least-square support vector machine and an autoregressive integrated moving average for forecasting electricity demand. The study results indicate that the proposed model can generate more realistic and reliable forecasts.…”

Section: Methodsmentioning

confidence: 99%

Using the LSTM Network to Forecast the Demand for Electricity in Poland

Manowska

2020

Applied Sciences

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The impact of environmental regulations introduced by the European Union is of key importance for electricity generation systems. The Polish fuel structure of electricity production is based on solid fuels. Moreover, the generating base is outdated and must gradually be withdrawn from the power system. In this context, Poland’s energy policy is undergoing a transformation as climate and environmental regulations are becoming increasingly stringent for the energy sector based on solid fuels (hard coal and lignite). However, the transformation process must be adapted to market demands, because the overriding goal is to ensure energy security by maintaining the continuity of energy supplies and an acceptable electricity price. This directly contributes to the development of the entire economy and the standard of living of the society, in accordance with the European Agreement establishing an association between the Republic of Poland and the European Communities and their Member States, signed on 16 December 1991, and the European Energy Charter, signed on 17 December 1991. Ensuring energy security is the most important goal of the energy policy. Therefore, energy companies must forecast the demand. The main goal of this article is to develop a mathematical model of electricity consumption by 2040 by all sectors of the economy: industry, transport, residential, commercial and public services, agriculture, forestry, and fishing. In order to achieve the intended goal, a model was developed by using Long Short-Term Memory (LSTM) artificial neural networks, which belong to deep learning techniques and reflect long-term relationships in time series for a small set of statistical data. The results show that the proposed model can significantly improve the accuracy of forecasts (1–3% of mean absolute percentage error (MAPE) for the analyzed sectors of the economy).

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A hybrid approach based on autoregressive integrated moving average and least-square support vector machine for long-term forecasting of net electricity consumption

Cited by 136 publications

References 40 publications

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management Part 1. Thermal Efficiency

Optimization of a 660 MWe Supercritical Power Plant Performance—A Case of Industry 4.0 in the Data-Driven Operational Management. Part 2. Power Generation

Using the LSTM Network to Forecast the Demand for Electricity in Poland

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