Accuracy of different machine learning algorithms and added-value of predicting aggregated-level energy performance of commercial buildings

As with many other sectors, to improve the energy performance and energy neutrality requirements of individual buildings and groups of buildings, built environment is also making use of machine learning for improved energy demand predictions. The goal of achieving energy neutrality through maximized use of on-site produced renewable energy and attaining optimal level of energy performance at building-cluster level requires reliable short term (resolution shorter than one day) energy demand predictions. However, the prediction and analysis of the energy performance of buildings is still focused on the individual building level and not on small neighborhood scale or building clusters. In a smart grid context, to better understand electricity consumption at different spatial levels, prediction should be at both individual as well as at building-cluster levels, especially for neighborhoods with definite boundaries (such as universities, hospitals). Therefore, in this paper, using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals. Predicting at hourly granularity is important to understand short-term dynamics, yet most of the neighborhood scale studies are limited to yearly, monthly, weekly, or daily data resolutions. Two years of data were used in training the model and the prediction was performed using another year of untrained data. Learning algorithms such as; boosted-tree, random forest, SVM-linear, quadratic, cubic, fine-Gaussian as well as ANN were all analysed and tested for predicting the electricity demand of individual and groups of buildings. The results showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity when metrics such as computational time and error accuracy are compared.</p

Towards individual thermal comfort : model predictive personalized control of heating systems

Automated user control for individual thermal comfor

Towards individual thermal comfort : model predictive personalized control of heating systems

Automated user control for individual thermal comfor

Multi-criteria feasibility assessment of cost-optimized alternatives to comply with heating demand of existing office buildings – a case study

In line with the EU's goal to phase out the use of fossil fuel, the Dutch government is determined to have gas-free new buildings from 2018 onwards. However, with over 90% of the heating demand in both existing residential and commercial buildings currently accomplished with natural gas, transitioning to a gas-free system in existing buildings remains an enormous challenge. Though electric heat pumps are gaining large ground as a substitute, the increase in electricity consumption also introduces uncertainties and further complexities to an already constrained electricity grid. This paper thus evaluates, the impact of switching to a greener demand side with all-electric heating systems for existing characteristics of the office buildings, with the current composition of the electricity production side in the Netherlands. Using multi-objective computational simulations with linear programming and feasibility assessment using the Kesselring method, the study reveals hybrid energy systems (utilizing electricity and gas) favors over all-electric energy systems for fulfilling the heating demand when the buildings are considered individually. This is because the switch to a greener heating system using electricity translates to a shift of demand for fossil energy from the building side to the central production side for the on-going situation in the Netherlands

Development of protease nanobiocatalysts and their application in hydrolysis of sunflower meal protein isolate

In this study, the suitability of fumed silica nanoparticles (FNS) and its derivatives (amino-modified FNS (AFNS), cyanuric chloride-activated AFNS (CCAFNS) and epoxy-modified FNS (GFNS)), for covalent immobilisation of two commercial protease preparations Alcalase(R) and Flavourzyme(R) was investigated. The highest hydrolytic activities of immobilised preparations were 25 IU g(-1) support (Alcalase-GFNS) and 2.95 IU g(-1) support (Flavourzyme-CCAFNS). Furthermore, the immobilised preparations showed 43% and 20% of initial specific activities of commercial protease preparations, respectively. Flavourzyme-CCAFNS also exhibited the highest exopeptidase activity of 22.83 L-pNAU g(-1) support. Finally, these two nanobiocatalysts were successfully applied for hydrolysis of sunflower meal protein isolate (SMPI), providing two times higher hydrolysis yields in comparison to free enzymes, justifying the applied immobilisation process. Namely, the highest hydrolysis yield (30%) was gained by the sequential hydrolysis with Alcalase-GFNS and Flavourzyme-CCAFNS, which resulted in the formation of small hydrophobic and hydrophilic peptides, lt = 5 kDa, confirmed by HPLC analysis and electrophoretic separation

Multi-criteria feasibility assessment of cost-optimized alternatives to comply with heating demand of existing office buildings – a case study

\u3cp\u3eIn line with the EU's goal to phase out the use of fossil fuel, the Dutch government is determined to have gas-free new buildings from 2018 onwards. However, with over 90% of the heating demand in both existing residential and commercial buildings currently accomplished with natural gas, transitioning to a gas-free system in existing buildings remains an enormous challenge. Though electric heat pumps are gaining large ground as a substitute, the increase in electricity consumption also introduces uncertainties and further complexities to an already constrained electricity grid. This paper thus evaluates, the impact of switching to a greener demand side with all-electric heating systems for existing characteristics of the office buildings, with the current composition of the electricity production side in the Netherlands. Using multi-objective computational simulations with linear programming and feasibility assessment using the Kesselring method, the study reveals hybrid energy systems (utilizing electricity and gas) favors over all-electric energy systems for fulfilling the heating demand when the buildings are considered individually. This is because the switch to a greener heating system using electricity translates to a shift of demand for fossil energy from the building side to the central production side for the on-going situation in the Netherlands.\u3c/p\u3

Neural network based predictive control of personalized heating systems

\u3cp\u3eThe aim of a personalized heating system is to provide a desirable microclimate for each individual when heating is needed. In this paper, we present a method based on machine learning algorithms for generation of predictive models for use in control of personalized heating systems. Data was collected from two individual test subjects in an experiment that consisted of 14 sessions per test subject with each session lasting 4 h. A dynamic recurrent nonlinear autoregressive neural network with exogenous inputs (NARX) was used for developing the models for the prediction of personalized heating settings. The models for subjects A and B were tested with the data that was not used in creating the neural network (unseen data) to evaluate the accuracy of the prediction. Trained NARX showed good performance when tested with the unseen data, with no sign of overfitting. For model A, the optimal network was with 12 hidden neurons with root mean square error equal to 0.043 and Pearson correlation coefficient equal to 0.994. The best result for model B was obtained with a neural network with 16 hidden neurons with root mean square error equal to 0.049 and Pearson correlation coefficient equal to 0.966. In addition to the neural network models, several other machine learning algorithms were tested. Furthermore, the models were on-line tested and the results showed that the test subjects were satisfied with the heating settings that were automatically controlled using the models. Tests with automatic control showed that both test subjects felt comfortable throughout the tests and test subjects expressed their satisfaction with the automatic control.\u3c/p\u3

Modelling hand skin temperature in relation to body composition

\u3cp\u3eSkin temperature is a challenging parameter to predict due to the complex interaction of physical and physiological variations. Previous studies concerning the correlation of regional physiological characteristics and body composition showed that obese people have higher hand skin temperature compared to the normal weight people. To predict hand skin temperature in a different environment, a two-node hand thermophysiological model was developed and validated with published experimental data. In addition, a sensitivity analysis was performed which showed that the variations in skin blood flow and blood temperature are most influential on hand skin temperature. The hand model was applied to simulate the hand skin temperature of the obese and normal weight subgroup in different ambient conditions. Higher skin blood flow and blood temperature were used in the simulation of obese people. The results showed a good agreement with experimental data from the literature, with the maximum difference of 0.31 °C. If the difference between blood flow and blood temperature of obese and normal weight people was not taken into account, the hand skin temperature of obese people was predicted with an average deviation of 1.42 °C. In conclusion, when modelling hand skin temperatures, it should be considered that regional skin temperature distribution differs in obese and normal weight people.\u3c/p\u3

Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data

In the energy renovation process, usually, buildings are upgraded to become energy-neutral annually with installed photovoltaic systems and heat pumps. However, the energy self-sufficiency of these buildings is surprisingly low. Therefore, the rapid deployment of heat pump based heating systems creates a shift of natural-gas consumption from the previously consumed building side (boilers) towards the electricity production side (power-plants). Fortunately, the development of information and communication technology enables access to consumption/generation data of building-related energy systems. Thus, there is an opportunity to strategically use this data and improve energy self-sufficiency and accommodate heat pump based heating systems. In this study, the improvement of self-sufficiency is discussed using a renovated neighborhood. The presented method incorporates a smart-grid application with a data-driven clustering, prediction, and an energy management strategy. First, clustering of similar demand-profiled dwellings with the k-means algorithm, and demand-prediction using the random-forest technique was performed. Afterwards, electric energy storage was introduced and multi-objective optimization reducing annualized costs and carbon emissions have been performed. For the carbon-dioxide optimal case, when aimed at the entire neighborhood, an annual self-sufficiency increment of more than 25% can be achieved, while four months out of the twelve being 100% energy self-sufficient