Adaptive building energy management with multiple commodities and flexible evolutionary optimization

Mauser, Ingo; Müller, Jan; Allerding, Florian; Schmeck, Hartmut

doi:10.1016/j.renene.2015.09.003

Cited by 51 publications

(25 citation statements)

References 25 publications

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“…Mauser et al [11] separate modifiability into two dimensions. The first dimension, which is called temporal degree of freedom, defines whether a device is deferrable or interruptible.…”

Section: Categorization Of Devices In Buildingsmentioning

confidence: 99%

“…This process of reoccurring optimization in BEMS shall be executed on computers that have a low energy consumption and thus small memory space and low computation power. Therefore, heuristics promise to achieve this task of optimization efficiently and in particular evolutionary algorithms have been used quite often, for instance in [11][12][13][14][15]. Other approaches also use other meta-heuristics, e.g., particle swarm optimization [16,17], to optimize the configuration [12] or the scheduling [11,[13][14][15][16][17] of devices.…”

Section: Heuristic and Multi-objective Optimization In Building Energymentioning

confidence: 99%

“…The device categories used in this paper are based on the categorizations in [11,20] and can be found in Table 1. Firstly, we identify whether the device is optimizable.…”

Section: Device Categoriesmentioning

confidence: 99%

“…Deferability is simply handled as a special form of interruptibility, which is limited to a single interruption at the very beginning of a possible operation cycle. In contrast to [11,20], the micro-CHP, which is usually a gas-fired engine that generates electricity and hot water simultaneously, i.e., working as power generator and central heating, is modeled similarly to the appliances. The micro-CHP is a device having unlimited interruptibility but no load-flexibility, whereas the air-conditioning has not only unlimited interruptibility but also loadflexibility in form of different cooling power levels.…”

Section: Device Categoriesmentioning

confidence: 99%

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Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Braun

Mauser

Schmeck

2016

Applications of Evolutionary Computation

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Abstract. The optimization of operating times and operation modes of devices and systems that consume or generate electricity in buildings by building energy management systems promises to alleviate problems arising in today's electricity grids. Conflicting objectives may have to be pursued in this context, giving rise to a multi-objective optimization problem. This paper presents the optimization of appliances as well as heating and air-conditioning devices in two distinct settings of smart buildings, a residential and a commercial building, with respect to the minimization of energy costs, CO2 emissions, discomfort, and technical wearout. We propose new encodings for appliances that are based on a combined categorization of devices respecting both, the optimization of operating times as well as operation modes, e.g., of hybrid devices. To identify an evolutionary algorithm that promises to lead to good optimization results of the devices in our real-world lab environments, we compare four state-of-the-art algorithms in realistic simulations: ESPEA, NSGA-II, NSGA-III, and SPEA2. The results show that ESPEA and NSGA-II significantly outperform the other two algorithms in our scenario.

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“…Mauser et al [11] separate modifiability into two dimensions. The first dimension, which is called temporal degree of freedom, defines whether a device is deferrable or interruptible.…”

Section: Categorization Of Devices In Buildingsmentioning

confidence: 99%

Section: Heuristic and Multi-objective Optimization In Building Energymentioning

confidence: 99%

“…The device categories used in this paper are based on the categorizations in [11,20] and can be found in Table 1. Firstly, we identify whether the device is optimizable.…”

Section: Device Categoriesmentioning

confidence: 99%

Section: Device Categoriesmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Braun

Mauser

Schmeck

2016

Applications of Evolutionary Computation

Self Cite

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“…The expression formulated in Equation (6) to evaluate the primary energy saving differs from the formulas proposed by the Directive 2004/8/EC, and it is worthwhile justifying the introduction of this expression. In the Annex II to the Directive, a Primary Energy Saving index is defined as follows: …”

Section: Appendix Amentioning

confidence: 99%

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

et al. 2016

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Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and prices on the optimal plant design and operation is proposed. With reference to a hotel building, a number of realistic scenarios is developed, exploring all the most frequent errors occurring in the estimation of energy loads and prices. Then, profit-oriented optimizations are performed for the examined scenarios, by means of a deterministic mixed integer linear programming algorithm. From a comparison between the achieved results, it emerges that: (i) the plant profitability is prevalently influenced by the average "spark-spread" (i.e., ratio between electricity and fuel price) and, secondarily, from the shape of the daily price profiles; (ii) the "optimal sizes" of the main components are scarcely influenced by the daily load profiles, while they are more strictly related with the average "power to heat" and "power to cooling" ratios of the building.

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References

2018

Building Performance Analysis

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Adaptive building energy management with multiple commodities and flexible evolutionary optimization

Cited by 51 publications

References 25 publications

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

Comparison of Multi-objective Evolutionary Optimization in Smart Building Scenarios

On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation

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