This paper presents the development of a decision support system (DSS) for a low-voltage grid with renewable energy sources (photovoltaic panels and wind turbine) which aims at achieving energy balance in a pilot microgrid with less energy consumed from the network. The DSS is based on a procedural decision algorithm that is applied on a pilot microgrid, with energy produced from renewable energy sources, but it can be easily generalized for any microgrid. To underline the benefits of the developed DSS two case scenarios (a household and an office building with different energy consumptions) were analyzed. The results and throw added value of the paper is the description of an implemented microgrid, the development and testing of the decision support system on real measured data. Experimental results have demonstrated the validity of the approach in rule-based decision switching.

This paper proposes a complete solution, implementing a low-cost, energy-independent, network-connected, and scalable environmental air parameter monitoring system. It features a remote sensing module which provides environmental data to a cloud-based server and a software application for real-time and historical data processing, standardized air quality indices computations, and a comprehensive visualization of environmental parameters evolutions. A fully operational prototype was built around a low-cost micro-controller connected to low-cost air parameter sensors and a GSM modem, powered by a stand-alone renewable energy-based power supply. The associated software platform has been developed by using Microsoft Power Platform technologies. The collected data is transmitted from sensors to a remote server via the GSM modem using custom-built JSON structures. From there, data is extracted and forwarded to a database accessible to users through a dedicated application. The overall accuracy of the air quality monitoring system has been thoroughly validated both in controlled indoor environment and against a trusted outdoor air quality reference station. The proposed air parameters monitoring solution paves the way for future research actions, such as the classification of polluted sites or prediction of air parameter variations in the site of interest.