• Energy Research

Abstract The time-varying nature of the heating loads of public buildings creates scope for exploring strategies to improve the energy system efficiency and to reduce the energy consumption and system operating costs. A well-researched and refined energy system operation strategy based on time-varying heating load demands is proposed in this paper. The proposed strategy is more effective and efficient than the existing experience-based operation strategies used to run energy systems. With full consideration of the factors affecting building heating loads under various scenarios, a multi-objective particle swarm optimisation algorithm combined with a scenario analysis is presented in this paper. The system efficiency and operation cost are set as two basic objectives to generate a Pareto frontier, and the occupant thermal comfort level is the dominant consideration while selecting an optimal state point for the final operation strategy. Using this simplified decision-making process, this approach can simultaneously calculate both the starting sequence and parameter settings for an optimised operation of the heat supply units. An energy plant on a university campus in Tianjin was selected to implement and evaluate this optimisation strategy. The case study results show that, without compromising the requirements of the thermal comfort of the building occupants, the energy system operating cost can be reduced by 38.9%, with an increase by a factor of 2.24 in the system coefficient of performance when compared with the current experience-based operation strategies.

Abstract Commissioning is an effective means to reduce the energy consumption of cooling systems in buildings. However, owing to the uncertainty of the load, the deviation between the true value of the load and the predicted value may cause a mismatch between the cooling load and the cooling capacity of the commissioning strategy, resulting in low robustness of the commissioning strategy. Therefore, a low-cost cooling system commissioning strategy, which can effectively quantify the load uncertainty and ensure the robustness of the strategy, is proposed in this study. A Quantile Regression Neural Network (QRNN) model is established to obtain the uncertainty range of the cooling load of a building in the form of a probability distribution. The low-cost commissioning method under each working condition with different partial load rates is obtained using an optimisation algorithm. An entropy–weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) multicriteria decision-making method is applied to optimise the commissioning strategy for the highest guarantee rate and lowest energy consumption. Through the case study of an existing office building located in Inner Mongolia, northern China, it was concluded that the proposed commissioning strategy can reduce the building’s average daily energy consumption by 7.75%. The results indicate that compared with the deterministic commissioning strategy, this robust commissioning strategy can achieve a higher guarantee rate under various load demands. In particular, on days with high load demand, a high guaranteed rate and low energy consumption can be achieved simultaneously.

Abstract The accurate prediction of a building's electricity consumption can provide baselines for energy management and indicate the building's energy-saving potential. However, electricity utilization indicators based on the building area are no longer applicable because of the overall increase in the building area per person and occupant energy demand of buildings. To tackle this challenge, the building electricity consumption was split into ‘basic’ and ‘variable’ forms in this study and a two-part building electricity consumption prediction model based on human behavior was established. The basic electricity consumption is related to the building area, while the variable electricity consumption is related to the building occupancy. The probability function and Markov model were used to describe the electricity consumption caused by the randomness of occupancy in buildings. The model was validated using three campus buildings. Based on the comparison of the actual electricity bills of the campus buildings with the model prediction results, the model accuracy error is less than 5%. The results show that the building electricity consumption of a building has a growth limit when multiple people share a room, which is related to a person's initiative or ability to control the electricity use.

Abstract Community energy planning is necessary for sustainable development of energy systems. However, given a series of energy sources and energy utilization technologies, it is crucial to select the optimal energy supply system to obtain the maximum comprehensive benefits. This study attempts to extend a multi-criteria integrated evaluation method that considers the aspects of technology, economy, environment, and society for evaluating the planning schemes of distributed energy supply systems. First, the rank correlation analysis and entropy information method are adopted to obtain subjective and objective weights, respectively. A combination of the maximum entropy principle and the minimized weighed generalized distance to the ideal scheme are employed to obtain optimal weighting coefficients. Second, based on the different priorities of evaluation indexes, the optimal schemes are established based on the improved grey incidence evaluation method, and under the integrated evaluation, the optimized combined cooling/heating and power system is proven to be the best option for the given case study. The results show that the proposed multi-criteria integrated evaluation method is a simple and practical tool for community energy planning.

Abstract Cool skin technologies, including high-reflective coating and vertical greening, are beneficial for passive energy-saving in buildings and are particularly useful in the renovation of existing buildings. Based on a numerical heat transfer model, a technology framework combining a multi-objective particle swarm optimization algorithm and a multiple criteria decision-making method, is proposed. The proposed method can rapidly determine the optimal application solution of cool skin technologies on a building’s exterior walls. The optimal solution considers cost, energy-savings, and the environmental benefits. The technology framework has been explored in four typical cities in China using an office building as the prototype. The results demonstrate that, when comparing the performance of cool skin technologies, the differences in the wall orientations and the climate zones should be considered. Except for the buildings with winter heating demands in northern China, which should keep a bare wall in the south orientation, the prefabricated greening technology outperforms the climbing greening technology and the high-reflective coating technology in reducing the building cooling or heating load. With the geographical latitude decreases, the technical advantages of vertical greening become more significant.