![]() " Human-in-the-loop HVAC operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions,"Īpplied Energy, Elsevier, vol. Jung, Wooyoung & Jazizadeh, Farrokh, 2019." Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms,"Īpplied Energy, Elsevier, vol. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018." Future trends of residential building cooling energy and passive adaptation measures to counteract climate change: The case of Taiwan,"Īpplied Energy, Elsevier, vol. Huang, Kuo-Tsang & Hwang, Ruey-Lung, 2016." Energy saving estimation for plug and lighting load using occupancy analysis," Anand, Prashant & Cheong, David & Sekhar, Chandra & Santamouris, Mattheos & Kondepudi, Sekhar, 2019." A simulation and optimisation methodology for choosing energy efficiency measures in non-residential buildings," Ceballos-Fuentealba, Irlanda & Álvarez-Miranda, Eduardo & Torres-Fuchslocher, Carlos & del Campo-Hitschfeld, María Luisa & Díaz-Guerrero, John, 2019." Using machine learning techniques for occupancy-prediction-based cooling control in office buildings,"Īpplied Energy, Elsevier, vol. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018." Smart energy systems for sustainable smart cities: Current developments, trends and future directions,"Īpplied Energy, Elsevier, vol. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019." A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings,"Īpplied Energy, Elsevier, vol. " Multiplexed real-time optimization of HVAC systems with enhanced control stability,"Īpplied Energy, Elsevier, vol. Asad, Hussain Syed & Yuen, Richard Kwok Kit & Huang, Gongsheng, 2017." Model-based predictive control of an ice storage device in a building cooling system,"Īpplied Energy, Elsevier, vol. The findings indicate that it is feasible to use the deep learning approach to predict equipment heat emission for achieving effective building energy management therefore to reduce building energy demand. While maintaining thermal comfort levels, up to 19% annual cooling energy demand reduction can be achieved by the proposed strategy when compared to that for the building managed by a static scheduled heating, ventilation and air-conditioning system, where in the studies, we focus on three types of equipment - computer, printer and kettle that are widely used in the office buildings. It was found that the model can perform equipment detection with an accuracy of 89.3%. ![]() ![]() ![]() Experiments were conducted in typical offices to generate the corresponding heat gain profiles, and then these were used in building simulation software to assess building performance. Subsequently, the data can be fed into building energy management systems through the formation of equipment heat gain profile therefore, building energy usage can be effectively managed. This project aims to develop a deep learning-based approach which enables the detection and recognition of equipment usage and the associated heat emissions in office spaces. Office buildings are likely to have higher cooling demands in the future due toincreasing use of equipment, emphasising the need to develop systems which can better understand (and reduce) the impact of internal gains from equipmentand adapt to actualrequirements. While the presence of occupants and how they use equipment contribute to the internal energy demand and affect the thermal environment. Previous works have shown that a large amount of energy is wasted in under- or over-utilized spaces since typicalbuilding management systems function based on fixed or static operation schedules. Building energy consumption accounts for a large proportion of energy use globally.
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