A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science

Recent techniques used in home energy management systems: a review

Power systems are going through a transition period. Consumers want more active participation in electric system management, namely assuming the role of producers–consumers, prosumers in short. The prosumers’ energy production is heavily based on renewable energy sources, which, besides recognized environmental benefits, entails energy management challenges. For instance, energy consumption of appliances in a home can lead to misleading patterns. Another challenge is related to energy costs since inefficient systems or unbalanced energy control may represent economic loss to the prosumer. The so-called home energy management systems (HEMS) emerge as a solution. When well-designed HEMS allow prosumers to reach higher levels of energy management, this ensures optimal management of assets and appliances. This paper aims to present a comprehensive systematic review of the literature on optimization techniques recently used in the development of HEMS, also taking into account the key factors that can influence the development of HEMS at a technical and computational level. The systematic review covers the period 2018–2021. As a result of the review, the major developments in the field of HEMS in recent years are presented in an integrated manner. In addition, the techniques are divided into four broad categories: traditional techniques, model predictive control, heuristics and metaheuristics, and other techniques.info:eu-repo/semantics/publishedVersio

Forecast-based Energy Management Systems

The high integration of distributed energy resources into the domestic level has led to an increase in the number of consumers becoming prosumers (producer + customer), which creates several challenges for network operators, such as controlling renewable energy sources over-generation. Recently, self-consumption as a new approach is encouraged by several countries to reduce the dependency on the national grid. This work presents two different Energy Management System (EMS) algorithms for a domestic Photovoltaic (PV) system: (a) real-time Fuzzy Logic-based EMS (FL-EMS) and (b) day-ahead Mixed Integer Linear Programming-based EMS (MILP-EMS). Both methods are tested using the data from the Active Office Building (AOB) located in Swansea University, Bay Campus, UK, as a case study to demonstrate the developed EMSs. AOB comprises a PV system and a Li-ion Battery Storage System (BSS) connected to the grid. The MILP-EMS is used to develop a Community Energy Management System (CEMS) to facilitate local energy exchange. CEMS is tested using the data from six houses located in London, UK, to form a community. Each household comprises a PV system and BSS connected to the grid. It is assumed that all six households use an EV and are equipped with a bidirectional charger to facilitate the Vehicle to House (V2H) mode. In addition, two shiftable appliances are considered to shift the demand to the times when PV generation is maximum to maximise community local consumption. MATLAB software is used to code the proposed systems. The FL-EMS exploits day-ahead energy forecast (assumed it is available from a third party) to control the BSS with the aim of reducing the net energy exchange with the grid by enhancing PV self-consumption. The FL-EMS determines the optimal settings for the BSS, taking into consideration the BSS's state of health to maximise its lifetime. The results are compared with recently published works to demonstrate the effectiveness of the proposed method. The proposed FL-EMS saves 18% on total energy costs in six months compared to a similar system that utilises a day-ahead energy forecast. In addition, the method shows a considerable reduction in the net energy exchanged between the AOB and the grid. The main objective of the MILP-EMS is to reduce the net energy exchange with the grid by including a two days-ahead energy forecast in the optimisation process. The proposed method reduces the total operating costs (energy cost + BSS degradation cost) by up to 35% over six months and reduces net energy exchanged with the grid compared to similar energy optimisation technique. The proposed cost function in MILP-EMS shows that it can outperform the performance of alternative cost function that directly reduce the net energy exchange. CEMS uses two days-ahead energy forecast to reduce the net energy exchange with the grid by coordinating the distributed BSSs. The proposed CEMS reduces the total operating costs (energy costs + BSSs degradation costs) of the community by 7.6% when compared to the six houses being operated individually. In addition, the proposed CEMS enhances community self-consumption by reducing the net energy exchange with the grid by 25.3% over four months compared to similar community energy optimisation technique. A further reduction in operating costs is achieved using V2H mode and including shiftable appliances. Results show that introducing the V2H mode reduces both the total operating costs of the community and the net energy exchange with the grid