Improving sustainable office building operation by using historical data and linear models to predict energy usage

Safa, Majeed; Safa, Mahdi; Allen, Jeremy J.; Shahi, Arash; Haas, Carl T.

doi:10.1016/j.scs.2016.12.001

Cited by 32 publications

(18 citation statements)

References 57 publications

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“…ANN has shown to be more effective than MLR for modeling the residential and commercial energy consumption, as described in [59,60]. Although ANN has a higher level of accuracy, MLR models are simple and understandable for non-professionals [61]. Moreover, ANN has been found to be more accurate than Linear regression, support vector machine [62] and, MARS [63] methodologies for modeling nonlinear fluctuating phenomena [64].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

et al. 2020

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Energy has a strategic role in the economic and social development of countries. In the last few decades, energy demand has been increasing exponentially across the world, and predicting energy demand has become one of the main concerns in many countries. The residential and commercial sectors constitute about 34.7% of global energy consumption. Anticipating energy demand in these sectors will help governments to supply energy sources and to develop their sustainable energy plans such as using renewable and non-renewable energy potentials for the development of a secure and environmentally friendly energy system. Modeling energy consumption in the residential and commercial sectors enables identification of the influential economic, social, and technological factors, resulting in a secure level of energy supply. In this paper, we forecast residential and commercial energy demands in Iran using three different machine learning methods, including multiple linear regression, logarithmic multiple linear regression methods, and nonlinear autoregressive with exogenous input artificial neural networks. These models are developed based on several factors, including the share of renewable energy sources in final energy consumption, gross domestic production, population, natural gas price, and the electricity price. According to the results of the three machine learning methods applied in our study, by 2040, Iranian residential and commercial energy consumption will be 76.97, 96.42 and 128.09 Mtoe, respectively. Results show that Iran must develop and implement new policies to increase the share of renewable energy supply in final energy consumption.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

et al. 2020

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“…/ The author(s) of this article declare that the materials and methods used in this study do not require ethical committee permission and/or legal-special permission. Enerji tüketimi ve sera gazı emisyonlarındaki önemli artışlar, plansız nüfus artışı ve yaşam standartlarının artması nedeniyle, günümüzün sürdürülebilir kalkınmasında enerji tasarrufunun çok önemli bir konu olduğunu göstermiştir [1]. Özellikle sürdürülebilir yapı tasarımı, bir kentin sürdürülebilir kalkınmasında önemli bir rol oynamaktadır.…”

Section: Etik Standartların Beyanı (Declaration Of Ethical Standards)unclassified

XPS Yalıtımlı Dış Duvarların Isıl Performanslarının Deneysel İncelenmesi

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Aruntaş

2021

Politeknik Dergisi

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Bu çalışmada; XPS ısı yalıtım levhaları kullanılarak tuğla ile örülmüş dış duvarların ısıl performansları deneysel olarak araştırılmıştır. Duvarların yapımında 13.5 cm kalınlığında yatay delikli tuğla kullanılmıştır. XPS ısı yalıtım levha kalınlığı olarak üç farklı kalınlık seçilmiştir. Dış duvarların ısıl performansı, sıcak kutu yöntemi (EN ISO 8990'a göre) kullanılarak belirlenmiştir. Sıcak kutu deneyinde dış ortam sıcaklığı-5 o C ve iç ortam sıcaklığı 27 o C olarak seçilmiştir. Deneysel çalışma için beş farklı duvar yapılmıştır. Bunlar sırasıyla; 1 adet yalıtımsız ve iç yüzeyi sıvalı, 1 adet yalıtımsız ve her iki yüzeyi sıvalı ve 3 adet XPS levha ile yalıtım (3, 4 ve 5 cm kalınlıkta) yapılmış duvarlardır. Soğuk ve sıcak bölümlerdeki duvar yüzey sıcaklıkları 480 dakika boyunca ölçülmüş ve EN ISO 8990'a göre duvarların toplam ısıl iletkenlik katsayısı (U değeri) hesaplanmıştır. XPS ile ısı yalıtımı yapılan duvarlarda en uygun ısı yalıtım malzemesi kalınlığı 5 cm ve U değeri 1.943 W/m 2 K olarak elde edilmiştir. Yalıtımsız duvarlar arasında tek yüzeyi sıvalı (KD-1) ve iki yüzeyi sıvalı (KD-2) duvarlarının U değerleri, sırasıyla 4.565 W/m 2 K ve 4.364 W/m 2 K olarak elde edilmiştir. İstatistiksel analiz sonucuna göre, XPS ısı yalıtım malzemesi kullanılan duvarlarda yalıtım kalınlığındaki artışı belirgin bir fark ortaya çıkarmamış ve XPS yalıtımlı duvarlar aynı grup içerisinde yer almıştır. XPS yalıtımlı duvarların yalıtımsız kontrol duvarlarına göre ısıl performansının oldukça yüksek olduğu belirlenmiştir.

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“…Case study Exp. Tool MB/DD Control [4] Commercial Building Yes E+ None Yes [5] Commercial building Yes E+ None Yes [6] Commercial building Yes E+ MB No [7] 2 office buildings and Yes None MB No 1 residential building [8] 2 commercial buildings n/a E+ MB No [9] Residential area No None MB Yes [10] 2 residential buildings Yes E+ MB Yes [11] 3 residential buildings No E+ MB Yes [12] 6 commercial buildings Yes E+ MB Yes [13] Residential building Yes None MB Yes [14] Commercial building No E+ MB-DD No [15] 2 commercial buildings Yes E+ MB-DD No [16] Office building Yes None DD No [17] Office building Yes E+ DD No [18] Residential house Yes TRANSYS DD No [19] Residential building Yes None DD No [20] Office building No E+ DD No [21] Commercial building Yes + RI None DD Yes [22] Living lab (1 room) Yes + RI None DD Yes [23] Commercial building Yes + RI None DD Yes [24] Residential house Yes None DD Yes [25] 9 commercial buildings No E+ DD Yes [26] Commercial building No E+ DD Yes scheme. In [22], an approach based on reinforcement learning, called Model-Assisted Batch Reinforcement Learning, is considered to provide data-driven control for the demand response problem in HVAC systems.…”

Section: Refmentioning

confidence: 99%

“…7(b). Marked in blue is the response when the optimal input is applied to the power predictive model of the Random Forest (16) and in red is the response when the same input is applied to the power predictive model of EnergyPlus. Since the optimal input is computed using the power predictive model of the Random Forest the blue trajectory perfectly follows the tracking signal.…”

Section: Power Managementmentioning

confidence: 99%

Data-driven model predictive control using random forests for building energy optimization and climate control

et al. 2018

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Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability, and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study, we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study, we implement and test DPC on real data from an off-grid house located in L' Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate the robustness of our method to uncertainties both in real data acquisition and weather forecast.

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Improving sustainable office building operation by using historical data and linear models to predict energy usage

Cited by 32 publications

References 57 publications

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

Machine Learning Modeling for Energy Consumption of Residential and Commercial Sectors

XPS Yalıtımlı Dış Duvarların Isıl Performanslarının Deneysel İncelenmesi

Data-driven model predictive control using random forests for building energy optimization and climate control

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