We provide early-stage net-zero energy design strategies for clients' architects and developers who are in the pre-planning application stages of their design journey.  Planning Authorities are increasingly asking for Energy and Sustainability Statements to accompany the architect's Design and Access Statement. 

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Working as Energy and Sustainability Consultants, and with mechanical and electrical systems consultancy expertise, we are experienced in the compilation of Multi-Objective parameter analysis to aid the search for optimal cost zero energy and zero carbon solution finding.

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Conventional approaches to finding the best or lowest carbon design solution for a given building usually rely on the experience of the designer or engineer alone. The unitary engineer will have a personal approach to the execution of the design of a low or zero-carbon building, which will bring about a set of solutions with which he/she is familiar from their own experience (see Figure 1 below).

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Where the problem is shared with the rest of the design team, then a larger set of possible solutions is brought to the design table. But even this may not have within it the best possible solution to the problem from the client's perspective  who might say “how do we make this building zero carbon for the lowest possible investment cost?”.

 

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Using new multi- objective search Evolutionary Algorithm (EA) mathematical techniques, we can begin to test some previous unexplored solutions illustrated in the Venn Diagram above.  On a purely random trial and error basis the EA algorithm selects a range of possible solutions. In the case of a building they will be  mutated variants of the building with the same geometry, but perhaps a different specification for the walls, glass, roof, orientation etc.  Each new mutated parent solution will be formed to form pairs, a dialectic, and the process will compute direct the pairs of solutions before forming a synthesis of the two solutions, which if stronger than the parents it will keep and place in a pool of strong solutions, and if weaker than the parent it will disregard and kill from any further computation.

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The strongest solutions are then plotted on a graph against to two main parameters, say cost versus carbon emissions.  Every dot represents a full simulation test of the mutated building specification. Over time a boundary line of strong solutions called a Pareto Front forms (see Figure 2 below).

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The pareto front is a boundary defining the limit of the best available combinations of carbon dioxide output and price.  Each solution on the pareto front will be the best carbon emission capable for that price point on the curve.  It becomes possible to select a set of solutions for a given budget.  Engineers and designers are normally interested in exploiting the points near the curve of the “hockey stick shape”. It is from here that any further increase in budget begins to yield diminishing returns. 

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The base software is a commercially available conventional thermal modelling dynamic energy simulation package available in the UK.  The algorithm selects the solution sets to be calculated and hands them over to the energy simulation calculation engine to compute the energy and carbon use, then accepts the results back and processes them through the EA algorithm.

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To direct the EA algorithm to look in the right place for possible solutions we give it what are called constraints.  The first constraint is to select the two main parameters we want compared. i.e the axis of the pareto front graph.

Fig 3 below shows the possible parameter objectives that can be directly compared against each other.

 

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