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Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year−1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year−1, and the district heating company would recover 176 GWh year−1 less heat from treated water.

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The concept of symbiosis, a mutually beneficial relationship, can be applied to food and energy systems. Greenhouse systems and biogas plants are interesting technologies for food–energy symbiosis, because both are usually based in rural areas and offer opportunities for the exchange of materials (e.g., biomass waste from the greenhouse as input to biogas plants) and energy (heat from biogas co‐generation for heating greenhouses). In this paper, the focus lies on manure resources for biogas in Switzerland, because manure amounts are high and currently largely underused. We provide a spatial analysis of the availability of manure as feedstock to biogas plants and heat source for greenhouses. In this feasibility study, we coupled the potential waste heat supply from manure‐based biogas and the greenhouse peak heat demand. We quantified the area‐based greenhouse heating demand for year‐around tomato production (from 0.98 to 2.67 MW ha−1 where the farms are located) and the available heat supply from manure‐based biogas (up to 3,200 GJ a−1 km−2). A total maximum greenhouse area of 104 ha could be sustained with manure‐based biogas heat, producing 20,800 tonnes a−1 tomatoes. This amounts to 11% of the total domestic tomato demand. Although the results are specific to Switzerland, our method can be adapted and also applied to other regions.

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