An Adaptive Random Forest Model for Predicting Demands and Solar Power of a Real Integrated Energy System

In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed. Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. <a>This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California. </a><a>The adaptive random forest model can provide a probability distribution of the prediction results. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results.</a> Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy.

An Adaptive Random Forest Model for Predicting Demands and Solar Power of a Real Integrated Energy System

In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed. Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. <a>This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California. </a><a>The adaptive random forest model can provide a probability distribution of the prediction results. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results.</a> Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy.

Application of Alternative Energy Trigeneration for Balancing of Loads and Sustainable Quality Assurance in Smart Energy Networks

The object of the research is micro-grids in district heating systems and the end consumers of the thermal energy from these systems. Topicality of the Thesis: The National Energy and Climate Plan of Latvia for 2030. The aim of the Doctoral Thesis is to design the energy management model for micro-grids with passive buildings and ecological trigeneration by using renewable energy resources.

Energy Communities: How Tools Can Facilitate Their Enhancement

This workshop brought together a selection of H2020 EU-funded projects to offer an overview of different tools used for the optimization of local energy networks and demonstrate how to facilitate grid interaction from the perspective of technology leaders representing four H2020 projects. This session offered a unique opportunity to discuss different approaches and compare the frameworks, practices, and tools used by different energy communities.

Active Participation of Buildings in the Energy Networks: Dynamic/Operational Models and Control Challenges

New advances in small-scale generation and consumption technologies have shifted conventional buildings’ functionality towards energy-efficient active buildings (ABs). Such developments drew the attention of researchers all around the world, resulting in a variety of publications, including several review papers. This study conducts a systematic literature review so as to analyse the concepts/factors enabling active participation of buildings in the energy networks. To do so, a relatively large number of publications devoted to the subject are identified, introducing the taxonomy of control and optimisation methods for the ABs. Then, a study selection methodology is proposed to nominate potential literature that has investigated the role of ABs in the energy networks. The modelling approaches in enabling flexible ABs are identified, while the potential challenges have been highlighted. Furthermore, the citation network of included papers is illustrated by Gephi software and analysed using “ForceAtlas2” and “Yifan Hu Proportional” algorithms so as to analyse the insights and possibilities for future developments. The survey results provide a clear answer to the research question around the potential flexibility that can be offered by ABs to the energy grids, and highlights possible prospective research plans, serving as a guide to research and industry.