Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Investigating the energy consumption in different operations of oilseed productions in Iran

The energy consumption in different operations of soybean, canola and sunflower productions in Golestan province of Iran was investigated. This study also focused sketches the environmental footprints of energy use in oilseed production. For these purpose Inquiries on 319 oilseed farms were conducted in 2009/10 production period. The results revealed that soybean gave the highest operational energy input (22235 MJ ha -1 ); while, total operational energy for canola and sunflower was relatively low as 8317 and 6013 MJ ha -1 , respectively. Irrigation operation consumed the highest share of total operational energy in soybean and sunflower productions; it was mainly in the form of electricity energy; however, in canola production, the tillage operation was the most intensive energy consumer, followed by harvesting practice. From this study it was found that increasing energy use efficiency of water pumping systems by good repair and maintenance and employing improved tillage and harvesting practices, such as low till agriculture, could be the pathways to make oilseed productions more environmental friendly and thus reduce their environmental footprints

Quantifying life cycle inventories of agricultural field operations by considering different operational parameters

PurposeThe ability to estimate fuel and lubricant consumption as well as depreciated weight of agricultural machinery used for field operations is very useful for energy and environmental analyses. In this study, life cycle inventory data of agricultural field operations were established by considering different parameters of such operations.MethodsAgricultural operations considered in this study include tillage, cultivation, planting, harvesting and post-harvest operations. For these operations, the fuel and lubricant consumption as well as depreciated weight of tractors, combine harvesters and agricultural implements was estimated by considering different operational parameters such as tractor power, field condition, depth of operation, soil condition, tractor type, operational capacity of machine, width of operation and speed. Technical standards were used to estimate different types of power required for most agricultural operations (drawbar power, rotary power and motion power). The standards were then used to evaluate the variability of the fuel and lubricant consumption as well as depreciated weight of the implements by varying the aforementioned parameters.Results and discussionThe results were compared to those that can be calculated with other approaches for life cycle inventory analysis of agricultural operations. Such comparison indicates that by using different parameters, representing the diverse local conditions of different field operations, a great variability of the results is obtained. For instance, diesel fuel consumption of tillage operations ranges from 12.6 to 76.0Lha(-1), with an average of 34.15Lha(-1) and standard deviation of 11.7Lha(-1). Such representativeness of the different conditions of each field operation cannot be modelled with other tools or via the use of standard LCI datasheets.ConclusionsThe final result of this study is a novel approach for the life cycle inventory analysis of agricultural operations, in terms of fuel and lubricant consumption and of depreciated weight of the machines, which are estimated by simply selecting the operational parameters which best represent the effect of local conditions

Application of multi-objective genetic algorithms for optimization of energy, economics and environmental life cycle assessment in oilseed production

In this study a multi-objective genetic algorithm (MOGA) was applied to find the best combination of mixing energy, economic and environmental indices concerning oilseed canola production. Data were collected from oilseed farming enterprises in Mazandaran province of Iran. Life cycle assessment of canola production from cradle to farm gate was investigated to calculate the environmental emissions. Econometric modelling was applied to find the relationship functions between energy inputs and three individual output parameters including environmental emissions, output energy and economic productivity. A multi-objective model was formulated in order to maximise the output energy and benefit to cost ratio, and minimise the final score of environmental emissions in order to obtain a set of Pareto frontier. When applying CML-IA methodology, multi-objective optimization resulted in a 32.1% reduction of the total environmental emissions as well as simultaneous increase of output energy and benefit cost ratio by 24.1% and 14.2%, respectively. More specifically, the reduction of chemicals by 82.2%, nitrogen by 11.1% and other chemical fertilisers by 70.7% would be beneficial from environment, energy and economic viewpoints. This work highlights the usefulness of the implementation of MOGA in agricultural production systems to find an optimized combination of mixing energy, economic and environmen