The purpose of this article is to develop a methodology to apply to multi-objective optimization algorithms aimed at energy efficiency in buildings, considering aspects such as incremental cost, energy consumption, greenhouse gas emissions and energy efficiency levels of lighting and air conditioning system, according to the mandatory technical regulation in public buildings in Brazil. Presenting a solution to assist in the decision making of engineers, architects or building managers for the optimal arrangements’ choice for lighting and air conditioning equipment, considering each built environment and project profile. For the validation process, a basic building was created with 15 rooms spread over three floors, according to the most common construction parameters in the North of Brazil. First, different combinations of objective-function candidates were investigated to compose the multi-objective algorithm fitness function, analyzing its performance in two central scenarios: (1) adding some “baits” in air conditioning equipment files, and (2) without this inclusion. Thus, it was found that considering only three objective functions—incremental cost, energy consumption and the air conditioning energy efficiency coefficient—it is possible to get optimal non-dominated solutions in both scenarios, thus highlighting the robustness of the proposed methodology.

Currently, for analyzing harmonic impacts on voltage at a point of interest, due to multiple nonlinear loads, the literature recommends carrying out simultaneous and synchronized measurement campaigns in all suspicious points with the use of high cost energy quality analyzers that are usually not available at the customers’ facilities and very often also not at the electric utilities. To overcome this drawback this paper proposes a method of assessing the harmonic impact due to multiple nonlinear loads on the total voltage harmonic distortion using only the load current true RMS values which are already available in all customers’ installations. The proposed methodology is based on Regression Tree technique using the Permutation Importance indicator which is validated in two case studies using two different electrical systems. The first case study is to ratify the use of Permutation Importance to measure the impact factor of each nonlinear load in a controlled scenario, the IEEE-13 bus test system, using ATP simulation (Alternative Transient Program). The second is to apply the methodology to a real system, an Advanced Measurement Infrastructure System (AMI) implanted on a campus of a Brazilian University, using low cost meters with only true RMS current measurements. The results achieved demonstrated the feasibility of applying the proposed methodology in real electric systems without the need for additional investments in high-cost energy quality analyzers.

No abstract