• Energy Research

Increasing demand for energy and emphasis on environmental sustainability has started to revolutionize the existing energy infrastructure within the built environment. In parallel, more distributed energy systems are rapidly springing up. These changes inevitably influence the design, operation and management of buildings. Recently, the energy and environmental evaluation of buildings for long-term decision-making and planning has shifted the boundaries from single buildings towards neighborhood scale. This is because buildings as a cluster can enhance the incorporation of distributed energy systems when realizing energy neutrality in the long run. However, when assessing the energy and environmental performance of infrastructural developments at the neighborhood level, the life-cycle aspect of energy systems is rarely considered. To understand the overall impacts from production to end-of-life stage, it is essential to assess the energy and environmental performance of clean energy initiatives from a life-cycle perspective. This paper proposes a novel decision support methodology by means of life cycle performance design-based approach to facilitate the planning process to realize energy neutral neighborhoods. The assessment methodology is developed based on scenario analysis through computational simulations. This is followed by a deterministic evaluation and the results let the decision-makers to select a suitable clean energy development scenario. The uncertainty of the selected scenario is scrutinized by performing a probabilistic sensitivity analysis using Monte Carlo simulations. A pragmatic case study has been analyzed and the results demonstrate the feasibility of exercising the proposed methodology in practice. The recommendations and limitations of realizing energy neutral neighborhoods are depicted subsequently.

Energy positive neighborhoods and cities are emerging concepts aimed at addressing the current energy and environmental sustainability challenges. In this paper, the concept and current research on energy hubs relating to energy positive neighborhoods are presented. In addition to discussing advantages and challenges of energy positive neighborhoods and energy hubs, opportunities for future research and development are also conversed.

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.

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.

Buildings are a key source of energy flexibility due to their high energy demand. Harnessing the energy flexibility of buildings, however, demands that buildings be considered collectively. This paper presents preliminary results to discover building's energy flexibility, from two different neighborhoods in the Netherlands. The energy demand profiles of three large buildings in each neighborhood are analyzed to identify possible useful simultaneous heating and cooling loads. Finally, the possibility for energy exchange between these buildings is explored.

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.