Computational intelligence techniques for HVAC systems: A review

Ahmad, Muhammad Waseem; Mourshed, Monjur; Yüce, Barış; Rezgui, Yacine

doi:10.1007/s12273-016-0285-4

Cited by 194 publications

(101 citation statements)

References 147 publications

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“…Artificial Neural Network (ANN) recently became highly popular for energy management in the built environment, which is highly complex and nonlinear [20,[36][37][38], primarily because of the strength of ANN in modelling complex systems. ANN mimics the biological neural system to find correlations for complex systems without having an explicit functional relationship [11].…”

Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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Abstract:This study presents a model for district-level electricity demand forecasting using a set of Artificial Neural Networks (ANNs) (parallel ANNs) based on current energy loads and social parameters such as occupancy. A comprehensive sensitivity analysis is conducted to select the inputs of the ANN by considering external weather conditions, occupancy type, main income providers' employment status and related variables for the fuel poverty index. Moreover, a detailed parameter tuning is conducted using various configurations for each individual ANN. The study also demonstrates the strength of the parallel ANN models in different seasons of the years. In the proposed district level energy forecasting model, the training and testing stages of parallel ANNs utilise dataset of a group of six buildings. The aim of each individual ANN is to predict electricity consumption and the aggregated demand in sub-hourly time-steps. The inputs of each ANN are determined using Principal Component Analysis (PCA) and Multiple Regression Analysis (MRA) methods. The accuracy and consistency of ANN predictions are evaluated using Pearson coefficient and average percentage error, and against four seasons: winter, spring, summer, and autumn. The lowest prediction error for the aggregated demand is about 4.51% for winter season and the largest prediction error is found as 8.82% for spring season. The results demonstrate that peak demand can be predicted successfully, and utilised to forecast and provide demand-side flexibility to the aggregators for effective management of district energy systems.

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Section: Artificial Neural Network For District Energy Managementmentioning

confidence: 99%

A Smart Forecasting Approach to District Energy Management

2017

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“…Many researchers have addressed the issue of controlling the air conditioning home systems through different control methods along the years [29,31]. A more generalized approach for mixed-integer predictive control of HVAC systems using (mixed integer linear programming) MILP is presented in [32].…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

et al. 2018

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Abstract:The growing demand for electricity is a challenge for the electricity sector as it not only involves the search for new sources of energy, but also the increase of generation capacity of the existing electrical infrastructure and the need to upgrade the existing grid. Therefore, new ways to reduce the consumption of energy are necessary to be implemented. When comparing an average house with an energy efficient house, it is possible to reduce annual energy bills up to 40%. Homeowners and tenants should consider developing an energy conservation plan in their homes. This is both an ecological and economically rational action. With this goal in mind, the need for the energy optimization arises. However, this has to be made by ensuring a fair level of comfort in the household, which in turn spawns a few control challenges. In this paper, the ON/OFF, proportional-integral-derivative (PID) and Model Predictive Control (MPC) control methods of an air conditioning (AC) of a room are compared. The model of the house of this study has a PV domestic generation. The recorded climacteric data for this case study are for Évora, a pilot Portuguese city in an ongoing demand response (DR) project. Six Time-of-Use (ToU) electricity rates are studied and compared during a whole week of summer, typically with very high temperatures for this period of the year. The overall weekly expense of each studied tariff option is compared for every control method and in the end the optimal solution is reached.

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“…The building sector plays an important role in this regard. About 40% of all primary energy is used in buildings all over the world [1][2][3][4][5]. The largest contributors to high energy consumption in buildings are heating, ventilation and air conditioning (HVAC) systems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…An optimization tool in studies is often connected with simulation program to find the best combination of design parameters. Simulation tools can predict the effects of design variables on building energy consumption [3]. The studies have been performed for residential buildings and for one or different climates using TRNSYS (University of Wisconsin, Madison, WI, USA) [21,22], EnergyPlus (U.S. Department of Energy's, Washington, DC, USA) [2,23,24], DOE-2 (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) [17,18], eQUEST (Energy-Models.com, San Francisco, CA, USA) [25] simulation programs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Energies

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Abstract:A properly designed house should provide occupants with the high level of thermal comfort at low energy demand. On many occasions investors choose to add additional insulation to the buildings to reduce heat demand. This may lead to overheating of the building without a cooling system in summer periods (these prevail in Poland). Additionally, it affects the deterioration of thermal comfort, which can only be improved by increasing ventilation. The paper presents the multi-objective optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs and thermal comfort is analyzed for the building without cooling. Infiltration changes during the simulation and is controlled by a special controller. Its task is to imitate the behavior of occupants in changing the supply airflow. Optimal selection of the design parameters is carried out using Non-dominated Sorting Genetic Algorithm II (NSGA-II) by coupling the building performance simulation program EnergyPlus with optimization environment. For the single-family house, optimal values of design variables for three different criteria are presented.

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Computational intelligence techniques for HVAC systems: A review

Cited by 194 publications

References 147 publications

A Smart Forecasting Approach to District Energy Management

A Smart Forecasting Approach to District Energy Management

Model Predictive Control Home Energy Management and Optimization Strategy with Demand Response

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

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