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 efficiency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most effective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy efficiency at a very early design stage. On the other hand,efficient 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, artificial intelligence (AI) in general and machine learning (ML) techniques in specific terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artificial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.

/pdf/machine-learning-for-estimation-of-building-energy-15lzdvkc3w.pdf

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

Information about the patterns that govern the energy demand and onsite generation can generate significant savings in the range of 15–30% in most cases and thus is essential for the management of commercial building energy systems. Predominantly, heating and cooling in a building as well as the availability of solar and wind energy are directly affected by variables such as temperature, humidity and solar radiation. This makes energy management decision making and planning sensitive to the prevalent and future weather conditions. Research attempts are being made using a variety of statistical or physical algorithms to predict the evolution of the building load or generation in order to optimise the building energy management. The response of the building energy system to changes in weather conditions is inherently challenging to predict; nevertheless numerous methods in the literature describe and utilise weather predictions. Such methods are being reviewed in this study and their strengths, weaknesses and applications in commercial buildings at different prediction horizons are discussed. Furthermore, the importance of considering weather forecasting inputs in energy management systems is established by highlighting the dependencies of various building components on weather conditions. The issues of the difficulty in implementation of integrated weather forecasts at commercial building level and the potential added value through energy management optimisation are also addressed. Finally, a novel framework is proposed that utilises a range of weather variable predictions in order to optimise certain commercial building systems.

Optimisation of energy management in commercial buildings with weather forecasting inputs: A review

A method and apparatus is provided for controlling an energy storage medium connected to an environmental control system that is providing environmental conditioning. The controller includes an energy pricing data structure for storing a real-time energy pricing profile indicative of energy rates corresponding to time-varying production costs of energy. The controller also includes a storage medium containing rules that approximate optimal control trajectories of an energy cost function that is dependent upon the real-time energy pricing profile, with the rules governing the operation of the energy storage medium. In addition, the controller has an engine for generating a storage medium control signal based upon the real-time energy pricing profile and the rules whereby the energy storage medium is controlled with the storage medium control signal in order to minimize energy costs associated with environmental control system.

Real-time pricing controller of an energy storage medium

Development of room temperature and relative humidity linear parametric models for an open office using BMS data

Building modelers need simulation tools capable of simultaneously considering building energy use, airflow and indoor air quality (IAQ) to design and evaluate the ability of buildings and their systems to meet today's demanding energy efficiency and IAQ performance requirements. CONTAM is a widely-used multizone building airflow and contaminant transport simulation tool that requires indoor temperatures as input values. EnergyPlus is a prominent whole-building energy simulation program capable of performing heat transfer calculations that require interzone and infiltration airflows as input values. On their own, each tool is limited in its ability to account for thermal processes upon which building airflow may be significantly dependent and vice versa. This paper describes the initial phase of coupling of CONTAM with EnergyPlus to capture the interdependencies between airflow and heat transfer using co-simulation that allows for sharing of data between independently executing simulation tools. The coupling is accomplished based on the Functional Mock-up Interface (FMI) for Co-simulation specification that provides for integration between independently developed tools. A three-zone combined heat transfer/airflow analytical BESTEST case was simulated to verify the co-simulation is functioning as expected, and an investigation of a two-zone, natural ventilation case designed to challenge the coupled thermal/airflow solution methods was performed.

/pdf/coupling-the-multizone-airflow-and-contaminant-transport-5czan4kt9j.pdf

Coupling the Multizone Airflow and Contaminant Transport Software CONTAM with EnergyPlus Using Co-Simulation.

This paper describes a load profile prediction algorithm that has recently been developed for use in an optimal supervisory controller. The profile prediction algorithm is but one aspects of the overall control system, which minimizes electric power consumption in cold storage applications. A brief description of the overall controller is given. This description is followed by a more detailed presentation of the prediction algorithm. Performance of the algorithm is then illustrated with respect to both simulated and field test data. Finally, on-line performance of the algorithm is presented

On-line recursive estimation for load profile prediction

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On-line recursive estimation for load profile prediction