Artificial intelligence methods for energy use prediction
Апстракт
This paper covers the application of artificial intelligence in agriculture. Technology development has enabled measurement, collecting and processing high quality big data. These data can be successfully used to significantly improve numerous segments of agriculture sector. The accent in this paper is given on the models used for energy use prediction. The application of Artificial Neural Networks with different structure is presented, such as Feedforward Neural Network, Radial basis Function Network and Adaptive Neuro-Fuzzy Inference System. Support Vector Machine model is also shown. The improvements of individual models are elaborated, through the analysis of the ensemble and hybrid approach. All of the proposed models are capable of solving complex problem of prediction of energy use based on real, measured data. Ensemble and hybrid models are promising, as it has been shown that the prediction accuracy is improved by combining different single models in proposed manner.
Кључне речи:
agriculture / artificial intelligence / machine learning / energy use predictionИзвор:
ISAE 2023, 2023, 42-Финансирање / пројекти:
- Истраживање и развој опреме и система за индустријску производњу, складиштење и прераду поврћа и воћа (RS-MESTD-Technological Development (TD or TR)-35043)
- Иновативни приступ у примени интелигентних технолошких система за производњу делова од лима заснован на еколошким принципима (RS-MESTD-Technological Development (TD or TR)-35004)
Колекције
Институција/група
Mašinski fakultetTY - CONF AU - Sretenović, Aleksandra AU - Jovanović, Radiša PY - 2023 UR - https://machinery.mas.bg.ac.rs/handle/123456789/7296 AB - This paper covers the application of artificial intelligence in agriculture. Technology development has enabled measurement, collecting and processing high quality big data. These data can be successfully used to significantly improve numerous segments of agriculture sector. The accent in this paper is given on the models used for energy use prediction. The application of Artificial Neural Networks with different structure is presented, such as Feedforward Neural Network, Radial basis Function Network and Adaptive Neuro-Fuzzy Inference System. Support Vector Machine model is also shown. The improvements of individual models are elaborated, through the analysis of the ensemble and hybrid approach. All of the proposed models are capable of solving complex problem of prediction of energy use based on real, measured data. Ensemble and hybrid models are promising, as it has been shown that the prediction accuracy is improved by combining different single models in proposed manner. C3 - ISAE 2023 T1 - Artificial intelligence methods for energy use prediction SP - 42 UR - https://hdl.handle.net/21.15107/rcub_machinery_7296 ER -
@conference{ author = "Sretenović, Aleksandra and Jovanović, Radiša", year = "2023", abstract = "This paper covers the application of artificial intelligence in agriculture. Technology development has enabled measurement, collecting and processing high quality big data. These data can be successfully used to significantly improve numerous segments of agriculture sector. The accent in this paper is given on the models used for energy use prediction. The application of Artificial Neural Networks with different structure is presented, such as Feedforward Neural Network, Radial basis Function Network and Adaptive Neuro-Fuzzy Inference System. Support Vector Machine model is also shown. The improvements of individual models are elaborated, through the analysis of the ensemble and hybrid approach. All of the proposed models are capable of solving complex problem of prediction of energy use based on real, measured data. Ensemble and hybrid models are promising, as it has been shown that the prediction accuracy is improved by combining different single models in proposed manner.", journal = "ISAE 2023", title = "Artificial intelligence methods for energy use prediction", pages = "42", url = "https://hdl.handle.net/21.15107/rcub_machinery_7296" }
Sretenović, A.,& Jovanović, R.. (2023). Artificial intelligence methods for energy use prediction. in ISAE 2023, 42. https://hdl.handle.net/21.15107/rcub_machinery_7296
Sretenović A, Jovanović R. Artificial intelligence methods for energy use prediction. in ISAE 2023. 2023;:42. https://hdl.handle.net/21.15107/rcub_machinery_7296 .
Sretenović, Aleksandra, Jovanović, Radiša, "Artificial intelligence methods for energy use prediction" in ISAE 2023 (2023):42, https://hdl.handle.net/21.15107/rcub_machinery_7296 .