Figure 1.
![Extent of fuzzy based research in renewable energy modeling. “x” axis – denotes year; the origin is [2006,0]. “y” axis – denotes the techniques in increasing complexity. The techniques are 1 – fuzzy regression, 2 – neuro-fuzzy, ANFIS, 3 – fuzzy AHP, ANP, 4 – fuzzy clustering, 5 – fuzzy optimization, 6 – neuro-fuzzy DEA, 7 – fuzzy GA, 8 – neuro-fuzzy GA, 9 – fuzzy expert, 10 – neuro-fuzzy expert, 11 – fuzzy MCDM, 12 – fuzzy TOPSIS, VIKOR, 13 – fuzzy PSO, 14 – fuzzy honey bee optimization, 15 – fuzzy PSO, QPSO, Cuckoo optimization.
Note: the number inside the bubble indicates the technique and size of the bubble indicates the no. of research publication](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/6470700c83f1392090d69816/j_acee-2020-036_fig_001.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKCJEWVHKE%2F20251215%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251215T010853Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHkaDGV1LWNlbnRyYWwtMSJHMEUCIQCKkSuEIQ1Ja7d6ygEg7Xwvu8MdQXWP3jZmHFpbwz0VlwIgQDAsB77Pv38a9USCyErIi%2BF7NRoIDKIGi%2F3eVBbxEnkqvQUIQhACGgw5NjMxMzQyODk5NDAiDOz5xfOioKdKSx1qvCqaBY%2BXIALhMUomTCzwYio3o9i4MZ6z%2FbHlEpyRFGj%2BdopEFA7ueIYP4gmsxTlYy8VpHQ1ZzJf6YzMIp8LG5Yg%2FITKEKDpFcWshIU0GW4HexeL%2BHbHhB%2FDQ2lHmcGkDHZB019tLH8bopI%2Fm9%2BrN7cRF5DvKDlRld9qgiEsKK8XUXRotCDAP9WL%2FVvZJ53s9Dkgll%2BIEdTxqbxICVSuWF7nkAC92tc0TEAEsZA8A5t1TXF%2FWWV9O3GtqjsO%2FTEWaISlY88ICK3F%2F5PCr7gzkUQ9t1sGXsyEgQ%2B%2FFFVmlp1SFtdeapjKjO%2FoNBp1qF7Hw5cKpsylNvQzgJ851ew8BVmdDl2dI5RGCFtHinHm20nUK3n6r6uimWpJv0FgqaiLQn5zPNm52asl2bYseim%2BO4m7nwMqwaLsu4wbEQaIGKVhWwOjZ0OxSKf%2FdVLi%2BuIqirkZ%2FjuZZsXbTAIx5%2FKTa2IAJlAcivLhLBg7QmUfso6ddFalK%2B7i4mt%2Fk%2BRLq1YWdtq%2FG0Pkhf32e5w98%2BjYLQ6AlEGvOkBtPfmNROamz%2Fq2zbdiEqCc8NdXDBE3aXhnduwpkaPAzgC5SivvYtK08X%2FvUMgibN2IsC5unIbbvPN6OTL5q95JZu6pKKcgAAOK5k0v8HVAZ4nSZsM6ZasF2AEMtRduh4TfAolSPCCcMqsx0AUAiVEHbJ1fybhFQOoxrj2X30fwVNwIWGZ63o%2F0S77uTiPjevTI7nyGW%2FWOWPzk8WPV426TAiF9yIUyrpBdUua3UQMOJyrbTS%2BNHWEyP6OcnRCkHUSkOcDR%2B56dujqSK6zpt%2Bgx%2FQjbzTmwLJA%2B5YXLdHFLnzJj28gqIn5zsmh5ZmJebMjczAZsG2rmK3OKFu%2Fn6avY2XHKncBSjJTDOtP3JBjqxAQBIA2vGxo7mDpi3c5%2B0Gyz%2B28Nv22SL6SjngBzGvsv5lEh2eIT1HYPem3jwHL2onQO9ZaeUGtEBs3GnczRjp3rXHMVfOZv7orNh6CZw%2BgtLSwHxw%2BMhdLmBbG4BE26NqmAQs7MrzL9AOVAYYO7l5HKah1RKlrT%2F02fUyiloYZb7zaJ7IRePFHjG4%2BVyuvzCsDr5UJ6uXHNPWs%2F3zKqVGQz0pPDZ%2F09VVO3%2F6cb099TJlw%3D%3D&X-Amz-Signature=903e1f7f678ae255128c4f677c07bf87f44ebd0515b5f08ad742a50e3bb3543e&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Figure 2.
![Biogas installation with combined system of heat supply for thermal stabilization of the bioconversion process [3]](https://sciendo-parsed.s3.eu-central-1.amazonaws.com/6470700c83f1392090d69816/j_acee-2020-036_fig_002.jpg?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Content-Sha256=UNSIGNED-PAYLOAD&X-Amz-Credential=ASIA6AP2G7AKCJEWVHKE%2F20251215%2Feu-central-1%2Fs3%2Faws4_request&X-Amz-Date=20251215T010853Z&X-Amz-Expires=3600&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEHkaDGV1LWNlbnRyYWwtMSJHMEUCIQCKkSuEIQ1Ja7d6ygEg7Xwvu8MdQXWP3jZmHFpbwz0VlwIgQDAsB77Pv38a9USCyErIi%2BF7NRoIDKIGi%2F3eVBbxEnkqvQUIQhACGgw5NjMxMzQyODk5NDAiDOz5xfOioKdKSx1qvCqaBY%2BXIALhMUomTCzwYio3o9i4MZ6z%2FbHlEpyRFGj%2BdopEFA7ueIYP4gmsxTlYy8VpHQ1ZzJf6YzMIp8LG5Yg%2FITKEKDpFcWshIU0GW4HexeL%2BHbHhB%2FDQ2lHmcGkDHZB019tLH8bopI%2Fm9%2BrN7cRF5DvKDlRld9qgiEsKK8XUXRotCDAP9WL%2FVvZJ53s9Dkgll%2BIEdTxqbxICVSuWF7nkAC92tc0TEAEsZA8A5t1TXF%2FWWV9O3GtqjsO%2FTEWaISlY88ICK3F%2F5PCr7gzkUQ9t1sGXsyEgQ%2B%2FFFVmlp1SFtdeapjKjO%2FoNBp1qF7Hw5cKpsylNvQzgJ851ew8BVmdDl2dI5RGCFtHinHm20nUK3n6r6uimWpJv0FgqaiLQn5zPNm52asl2bYseim%2BO4m7nwMqwaLsu4wbEQaIGKVhWwOjZ0OxSKf%2FdVLi%2BuIqirkZ%2FjuZZsXbTAIx5%2FKTa2IAJlAcivLhLBg7QmUfso6ddFalK%2B7i4mt%2Fk%2BRLq1YWdtq%2FG0Pkhf32e5w98%2BjYLQ6AlEGvOkBtPfmNROamz%2Fq2zbdiEqCc8NdXDBE3aXhnduwpkaPAzgC5SivvYtK08X%2FvUMgibN2IsC5unIbbvPN6OTL5q95JZu6pKKcgAAOK5k0v8HVAZ4nSZsM6ZasF2AEMtRduh4TfAolSPCCcMqsx0AUAiVEHbJ1fybhFQOoxrj2X30fwVNwIWGZ63o%2F0S77uTiPjevTI7nyGW%2FWOWPzk8WPV426TAiF9yIUyrpBdUua3UQMOJyrbTS%2BNHWEyP6OcnRCkHUSkOcDR%2B56dujqSK6zpt%2Bgx%2FQjbzTmwLJA%2B5YXLdHFLnzJj28gqIn5zsmh5ZmJebMjczAZsG2rmK3OKFu%2Fn6avY2XHKncBSjJTDOtP3JBjqxAQBIA2vGxo7mDpi3c5%2B0Gyz%2B28Nv22SL6SjngBzGvsv5lEh2eIT1HYPem3jwHL2onQO9ZaeUGtEBs3GnczRjp3rXHMVfOZv7orNh6CZw%2BgtLSwHxw%2BMhdLmBbG4BE26NqmAQs7MrzL9AOVAYYO7l5HKah1RKlrT%2F02fUyiloYZb7zaJ7IRePFHjG4%2BVyuvzCsDr5UJ6uXHNPWs%2F3zKqVGQz0pPDZ%2F09VVO3%2F6cb099TJlw%3D%3D&X-Amz-Signature=aa62f28c505b657fe5d1568a8920e29b22582e4a4ace4c44709aa4109ac6082d&X-Amz-SignedHeaders=host&x-amz-checksum-mode=ENABLED&x-id=GetObject)
Figure 3.
Figure 4.
Classification of influence factors on the environmental- energy efficiency and term values as linguistic variables
| Influence factors | Designation and name of the linguistic variable | Terms for evaluating linguistic variables | |
|---|---|---|---|
| Environme ntal, X 1 | The effect of reducing carbon monoxide emissions in exergy units, x 11 | Low (L) | |
| The effect of preventing environmental pollution in exergy units, x 12 | Low (L) | ||
| The effect of using biofuels in exergy units, x 13 | Low (L) | ||
| The effect of the use of organic fertilizers in exergy units, x 14 | Low (L) | ||
| Influence factors | Designation and name of the linguistic variable | Terms for evaluating linguistic variables | |
| Exergy effect from receiving additional thermal energy from traditional sources, x 21 | Low (L) | ||
| Energy, X 2 | Exergy effect of obtaining additional solar energy, x 22 | Low (L) | |
| Exergy effect of receiving additional thermal energy from the heat pump, x 23 | Low (L) | ||
| Fermentation temperature regimes, X 3 | Exergy effect at: | Thermophilic mode, x 31 Mesophilic mode, x 32 Cryophilic mode, x 33 | Low (L) |