Smart Forecasting of Building and District Energy Management

Dear Colleagues,

Forecasting models are widely used in different domains, including in the context of building and district energy management. Moreover, forecasting plays an essential role in the control of power plants and electric power exchange in interconnected systems. It also supports energy planners in understanding the influence of some variables on energy consumption, and thus inform decision making. On a temporal scale, forecasting can be short-term, for instance, for balancing electricity supply, and long-term, including, for capacity expansion, capital investment return studies, and revenue analysis. Over the years, many different forecasting models have been applied for electricity and power predictions, such as multivariate and multiple regression, SVM, time series, and the autoregressive moving average. Equally, artificial neural networks (ANN) have become widely used for prediction scenarios. ANN has been used for various tasks, such as (a) short-term load forecasting in microgrids; (b) optimization scenarios at building level; and (c) long term horizon scenarios to determine the annual electricity consumption of a region, district, or building. Conversely, recent advances in information and communication technologies in areas such the Internet of things, semantics (including building information modelling), and artificial intelligence have paved the way to new promising methods, techniques, and tools. The adoption and impact of these technologies can only be achieved if adapted education and training strategies are implemented. 

This Special Issue aims to publish high-quality research articles on the latest developments in the smart forecasting of building and district energy management spanning the whole lifecycle—from design to operation and reuse/recycle, focusing on technology, policies, training and education, and practices. Articles addressing the interrelationships between traditionally disparate domains are particularly welcome. The topics include, but are not limited to, the following:

• Building simulation and optimization
• Distributed energy resources and storage
• Smart buildings, neighborhoods, and districts
• Computational intelligence and data analytics
• Policies and regulations
• Energy and water nexus
• Heating, cooling, and thermal comfort
• Climate change adaptation
• Behavioral aspects
• Building stock modelling and refurbishment
• Education and training in building energy
• Building information modelling for energy efficiency
• Training and education for energy efficiency

Prof. Dr. Yacine Rezgui
Dr. Sylvain Kubicki
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.