Prediction of indoor temperature in an institutional building

The importance of predicting building indoor temperature is inevitable to execute an effective energy management strategy in an institutional building. An accurate prediction of building indoor temperature not only contributes to improved thermal comfort conditions but also has a role in building heating and cooling energy conservation. To predict the indoor temperature accurately, Artificial Neural Network (ANN) has been used in this study because of its performance superiority to deal with the time-series data as cited in past studies. Network architecture is the most important part of ANN for predicting accurately without overfitting the data. In this study, as a part of determining the optimal network architecture, important input parameters related to the output has been sorted out first. Next, prediction models have been developed for building indoor temperature using real data. Initially, spring season of Australia was selected for data collection. During model development three different training algorithms have been used and the performance of these training algorithms has been evaluated in this study based on prediction accuracy, generalization capability and iteration time to train the algorithm. From results Lovenberg-Marquardt has been found the best-suited training algorithm for short-term prediction of indoor space temperature. Afterwards, residual analysis has been used as a technique to verify the validation result. Finally, the result has been justified by applying a similar approach to another building case and using two different weather data-sets of two different seasons: summer and winter of Australia

Probabilistic load forecasting for building energy models

In the current energy context of intelligent buildings and smart grids, the use of load forecasting to predict future building energy performance is becoming increasingly relevant. The prediction accuracy is directly influenced by input uncertainties such as the weather forecast, and its impact must be considered. Traditional load forecasting provides a single expected value for the predicted load and cannot properly incorporate the effect of these uncertainties. This research presents a methodology that calculates the probabilistic load forecast while accounting for the inherent uncertainty in forecast weather data. In the recent years, the probabilistic load forecasting approach has increased in importance in the literature but it is mostly focused on black-box models which do not allow performance evaluation of specific components of envelope, HVAC systems, etc. This research fills this gap using a white-box model, a building energy model (BEM) developed in EnergyPlus, to provide the probabilistic load forecast. Through a Gaussian kernel density estimation (KDE), the procedure converts the point load forecast provided by the BEM into a probabilistic load forecast based on historical data, which is provided by the building’s indoor and outdoor monitoring system. An hourly map of the uncertainty of the load forecast due to the weather forecast is generated with different prediction intervals. The map provides an overview of different prediction intervals for each hour, along with the probability that the load forecast error is less than a certain value. This map can then be applied to the forecast load that is provided by the BEM by applying the prediction intervals with their associated probabilities to its outputs. The methodology was implemented and evaluated in a real school building in Denmark. The results show that the percentage of the real values that are covered by the prediction intervals for the testing month is greater than the confidence level (80%), even when a small amount of data are used for the creation of the uncertainty map; therefore, the proposed method is appropriate for predicting the probabilistic expected error in load forecasting due to the use of weather forecast data

application of artificial neural networks to the simulation of a dedicated outdoor air system doas

Abstract Tables of performance of installed HVAC (Heating, Ventilation and Air Conditioning) devices are important in the development of consistent building energy audits and appropriate control strategies. However, given the possible complexity of HVAC devices and the need for the deployment to computational environments, tables of performance should be passed in a more complete and flexible format, compared with the current practices in the HVAC sector. In such a context, this paper describes the phases of development and application of Artificial Neural Networks (ANNs) aimed at the assessment of the performance of a Dedicated Outdoor Air System (DOAS). ANNs are well renowned because of their applications in many important fields such as autonomous driving systems, speech recognition, etc. However, they may be used also to calculate the output of complex phenomena (like the ones involved in HVAC components) and are characterized by a very flexible and comprehensive formulation which would be able to adapt to any HVAC component or system. In the frame of this study, three ANNs have been developed and tested, for the full description of the performance of a DOAS. The developed ANNs were trained by means of data coming from a proprietary software. The achieved ANNs showed robust and reliable behavior and ensure high accuracy (mean absolute errors usually below 0.1 K on temperatures and 0.3% on capacity and power) and flexibility. Moreover, in some cases, they may be used also for the identification of anomalous data present among the sets of training and validation data

Scaling energy management in buildings with artificial intelligence

L'abstract è presente nell'allegato / the abstract is in the attachmen

Data-Driven Key Performance Indicators and Datasets for Building Energy Flexibility: A Review and Perspectives

Energy flexibility, through short-term demand-side management (DSM) and energy storage technologies, is now seen as a major key to balancing the fluctuating supply in different energy grids with the energy demand of buildings. This is especially important when considering the intermittent nature of ever-growing renewable energy production, as well as the increasing dynamics of electricity demand in buildings. This paper provides a holistic review of (1) data-driven energy flexibility key performance indicators (KPIs) for buildings in the operational phase and (2) open datasets that can be used for testing energy flexibility KPIs. The review identifies a total of 81 data-driven KPIs from 91 recent publications. These KPIs were categorized and analyzed according to their type, complexity, scope, key stakeholders, data requirement, baseline requirement, resolution, and popularity. Moreover, 330 building datasets were collected and evaluated. Of those, 16 were deemed adequate to feature building performing demand response or building-to-grid (B2G) services. The DSM strategy, building scope, grid type, control strategy, needed data features, and usability of these selected 16 datasets were analyzed. This review reveals future opportunities to address limitations in the existing literature: (1) developing new data-driven methodologies to specifically evaluate different energy flexibility strategies and B2G services of existing buildings; (2) developing baseline-free KPIs that could be calculated from easily accessible building sensors and meter data; (3) devoting non-engineering efforts to promote building energy flexibility, such as designing utility programs, standardizing energy flexibility quantification and verification processes; and (4) curating datasets with proper description for energy flexibility assessments.Comment: 30 pages, 14 figures, 4 table