Application of Artificial Neural Networks for Virtual Energy Assessment

A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as ‘operational energy’ in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions.

Prospect of low-cost energy conservation in residential energy consumption

The energy sector of trinidad and tobago (T&T), with its nationally determined contributions (NDCs), is still struggling for its place in mainstream academic literature. The review paper aims to identify the prospects of low-cost energy conservation measures in the residential sector of T&T. The review follows a four-step review methodology to serve as a basis for creating policy and practice guidelines. First, review articles are checked for their quality on a 5-point scale on the mixed methods appraisal tool (MMAT) to check the quality of review articles. Second, Microsoft Excel, R (RQDA) package, and Voyant tools have been used to index code, analyse, and visualise data. The research trends on small islands developing states (SIDS) energy aspect in general and T&T specifically highlight the critical role of energy challenges related to economic and social development, emphasising technology, infrastructure development, and funds availability. Awareness of low-cost energy conservation measures has a high prospect in reducing residential consumption and balancing demand-side management. This paper contributes to facilitating policy direction on energy efficiency and energy conservation for T&T and other SIDS.

Design And Development Of Real-Time Small Scale IoT Based Energy Monitoring System

This paper presents the digitization of small-scale energy monitoring systems based on IoT. The proposed energy monitoring system known as EMOSY eliminates the high-cost energy meter. EMOSY is designed to be portable and practical to use without modification of internal or external connection of appliances. EMOSY is developed by using a voltage detector circuit concept by amplifying the existence of electrostatic. This electrostatic reading sends to the database through Wi-Fi module ESP8266 integrated with Arduino NodeMCU. The web page is designed using Adobe Dreamweaver with HTML and PHP coding. In the proposed system, the user able to monitor the energy usage of each appliance and estimated billing time to time. Based on the result, the energy monitoring system successfully can detect the existence of electrostatic, and the webpage database can display the energy usage extended to the estimated electricity bill. The monitoring system is found to be useful to the residential, commercial, and industrial to monitor energy patterns, which is essential to facilitate energy conservation measures for minimizing energy usage.

Microgrid for a Cluster of Grid Independent Buildings Powered by Solar and Wind Energy

The reduction of CO2 emissions and the avoidance of Global Climate Change necessitate the conversion of the electricity generation industry to rely on non-carbon sources. Additionally, the mitigation of the duck-curve effects in microgrids requires the development of grid-independent buildings. Computations were performed for a cluster of one thousand grid-independent buildings in the North Texas area, where air-conditioning demand is high in the summer months. The electricity demand is balanced with energy supply generated from wind turbines, photovoltaic cells, or stored energy in hydrogen tanks. The results indicate that with one wind turbine operating, each building must be fitted with 10.2 kW rating photovoltaics capacity and a tank with 5.2 m3 of hydrogen storage capacity to satisfy the hourly demand of the buildings’ community. The addition of more wind turbines significantly reduces the needed PV rating but increases the required storage. Investing in energy conservation measures in the buildings significantly reduces both the needed storage capacity and the PV cell ratings.

Smart Street Lighting System using IoT and Cloud Computing

Street lights account for the maximum energy expense for any city. A smart street lighting system can cut out lighting costs by almost 70%. A smart street lighting system is a system that adjusts light output based on usage and movement, i.e., automatically detecting movements of either pedestrians or vehicles in the vicinity. For effective street light management, we propose installing a wireless-based system to remotely track and control the energy consumption of street lights of a particular city in real-time and take necessary energy conservation measures through power conditioning and adaptive control. The proposed system should be installed on the street light poles which consist of a wireless module that continuously transmits data to back-end IT systems where we can uninterruptedly monitor the condition of the lights. We can control the LED street lighting based on traffic flow in that particular area. The data from the system can be transferred to a central server using wireless technology and Cloud to have global access to the system. We can automate the system to switch on or off the lights at specific timings. We can also easily identify any glitches or failures in our system because everything is connected over the Internet.

A multi-country study of household energy saving behaviour.

Due to the increased worldwide demand for electricity, governments and policy makers are looking to identify tools to help reduce household energy consumption. This study examines the relationship between the empirical and attitudinal variables and the likelihood of implementation of household energy conservation measures and investments, using a wide data set involving household level characteristic from 10,252 respondents from ten different countries (Australia, Canada, Czech Republic, France, Italy, Korea, Mexico, Netherlands, Norway, and Sweden). Moreover, the effects of time varying electricity pricing on household energy saving measures and investments is evaluated. Using the multivariable probit model, this project makes comparison across the ten countries incorporating socio-demographic characteristics, and allows for the potential correlation between energy saving decisions (measures and investments).

A multi-country study of household energy saving behaviour.

Due to the increased worldwide demand for electricity, governments and policy makers are looking to identify tools to help reduce household energy consumption. This study examines the relationship between the empirical and attitudinal variables and the likelihood of implementation of household energy conservation measures and investments, using a wide data set involving household level characteristic from 10,252 respondents from ten different countries (Australia, Canada, Czech Republic, France, Italy, Korea, Mexico, Netherlands, Norway, and Sweden). Moreover, the effects of time varying electricity pricing on household energy saving measures and investments is evaluated. Using the multivariable probit model, this project makes comparison across the ten countries incorporating socio-demographic characteristics, and allows for the potential correlation between energy saving decisions (measures and investments).

A multi-objective optimization analysis of passive energy conservation measures in a Toronto house.

This study presents a multi-objective optimization environment in which passive energy conservations measures of a high performance house in Toronto are evaluated. The optimization environment was created using the jEPlus software suite where the case study house acted as the reference building. The study house simulation model was calibrated using a data-driven procedure, and acceptable CV(RSME) and NMBE tolerances were reached in accordance with ASHRAE calibration requirements. The optimization varied passive energy efficiency parameters in search of configurations yielding optimal building performance and life cycle cost. The optimization results showed that energy savings of 33% relative to building code minimum were justified at the point of minimal life cycle cost via passive energy saving measures alone before considering active systems. These results suggest that improved thermal envelopes are economically advantageous with good building practice. However, they suggest that the current Passive House standard does not coincide with the economic minimum for the local economic and environmental climate.