The Sustainable Approaches of Energy Management and Intelligent Load Forecasting of HVAC Systems in Buildings

Dear Colleagues,

In recent years, there have been increasing concerns over the growth of energy demand, global warming, and the reduction in greenhouse gases. To achieve the goals set for carbon dioxide emissions’ reduction, energy consumption must be reduced by improving energy efficiency in industrial and commercial sectors. In this regard, the building sector has garnered increased attention because it consumes over 36% of the final energy consumption worldwide. The use of advanced control systems in the building sector enables an increase in the overall energy efficiency and a reduction in carbon and other emissions.

Intelligent buildings have garnered great interest due to the fast advancement of communication and information technology. These buildings can forecast weather, ambient temperature, and sun irradiation and modify heating, ventilation, and air conditioning (HVAC) operations appropriately, based on the current and previous data. The integration of monitoring system and advanced control techniques would increase energy saving and can avoid the identification of an anomaly in a system operation. This change is intended to predict energy load and reduce HVAC system energy usage while maintaining an appropriate degree of thermal comfort and indoor air quality.

This Special Issue aims to cover the most recent advances related to the theory, design, modelling, load forecasting, application, control, and condition monitoring of HVAC system. We encourage authors to submit research papers, reviews, technical papers, case studies, and methodologies.  

Topics of interest for publication include, but are not limited to, the following:

• Online and offline condition monitoring techniques;
• Optimal design methodologies;
• Advanced modelling approaches;
• Statistical forecasting models (ARIMA; SARIMA; ARMAX; multi-variate regression; Kalman filter; etc.);
• Artificial neural networks (ANNs);
• Knowledge-based expert systems and fuzzy theory and fuzzy inference systems;
• Evolutionary computation models and evolutionary algorithms;
• Support vector regression (SVR);
• New models for load forecasting demand;
• Important variables in the forecasting;
• Forecasting applications to the management of the HVAC system;
• Data analytics;
• Fault detection and diagnosis.

Dr. Ali Razban
Guest Editor

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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• energy management
• energy efficiency
• energy building
• HVAC system
• load forecasting
• thermal comfort
• artificial neural networks (ANNs)
• fuzzy theory