Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions and Research Directions

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand managemen

Economic and regulatory uncertainty in renewable energy system design: a review

Renewable energy is increasingly mobilizing more investment around the globe. However, there has been little attention to evaluating economic and regulatory (E&R) uncertainties, despite their enormous impact on the project cashflows. Consequently, this review analyzes, classifies, and discusses 130 articles dealing with the design of renewable energy projects under E&R uncertainties. After performing a survey and identifying the selected manuscripts, and the few previous reviews on the matter, the following innovative categorization is designed: sources of uncertainty, uncertainty characterization methods, problem formulations, solution methods, and regulatory frameworks. The classification reveals that electricity price is the most considered source of uncertainty, often alone, despite the existence of six other equally influential groups of E&R uncertainties. In addition, real options and optimization arise as the two main approaches researchers use to solve problems in energy system design. Subsequently, the following aspects of interest are discussed in depth: how modeling can be improved, which are the most influential variables, and potential lines of research. Conclusions show the necessity of modeling E&R uncertainties with currently underrepresented methods, suggest several policy recommendations, and encourage the integration of prevailing approaches.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No Contaminant::7.2 - Per a 2030, augmentar substancialment el percentatge d’energia renovable en el con­junt de fonts d’energiaObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version

Demand response performance and uncertainty: A systematic literature review

The present review has been carried out, resorting to the PRISMA methodology, analyzing 218 published articles. A comprehensive analysis has been conducted regarding the consumer's role in the energy market. Moreover, the methods used to address demand response uncertainty and the strategies used to enhance performance and motivate participation have been reviewed. The authors find that participants will be willing to change their consumption pattern and behavior given that they have a complete awareness of the market environment, seeking the optimal decision. The authors also find that a contextual solution, giving the right signals according to the different behaviors and to the different types of participants in the DR event, can improve the performance of consumers' participation, providing a reliable response. DR is a mean of demand-side management, so both these concepts are addressed in the present paper. Finally, the pathways for future research are discussed.This article is a result of the project RETINA (NORTE-01-0145- FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We also acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team, and grants CEECIND/02887/2017 and SFRH/BD/144200/2019.info:eu-repo/semantics/publishedVersio

Optimisation and Operation of Residential Micro Combined Heat and Power (μCHP) Systems

In response to growing concerns regarding global warming and climate change, reduction of CO2 emissions becomes a priority for many countries, especially the developed ones such as the UK. Residential applications are considered among the most important areas for substantial reduction of CO2 emissions because they represent a major part of the total consumed energy in those countries. For instance, in the UK, residential applications are currently accountable for about 150 Mt CO2 emissions, which represents approximately 25% of the whole CO2 emissions [1-2]. In order to achieve a significant CO2 reduction, many strategies must be adopted in the policy of these countries. One of these strategies is to introduce micro combined heat and power (μCHP) systems into residential energy systems, since they offer several advantages over traditional systems. A significant amount of research has been carried out in this field; however, in terms of integrating such systems into residential energy systems, significant work is yet to be conducted. This is because of the complexity of these systems and their interdependency on many uncertain variables, energy demand of a house is a case in point. In order to achieve such integration, this research focuses on the optimisation and operation of μCHP systems in residential energy systems as essential steps towards integration of these systems, so it deals with the optimisation and operation of a μCHP system within a building taking into account that the system is grid-connected in order to export or import electricity in certain cases. A comprehensive review that summarises key points that outline the trend of previous research in this field has been carried out. The reviewed areas include: technologies used as residential μCHP units, modelling of the μCHP systems, sizing of μCHP systems and operation strategies used for such systems. To further this, a generic model for sizing of μCHP system’s components to meet different residential application has been developed by the author. Two different online operation strategies of residential μCHP systems, namely: an online linear programming optimiser (LPO) and a real time fuzzy logic operation strategy (FLOS) have been developed. The performance of the novel online operation strategies, in terms of their ability to reduce operation costs, has been evaluated. Both the LPO and the FLOS were found to have their advantages when compared with the traditional operation strategies of μCHP systems in terms of operation costs and CO2 emissions. This research should therefore be useful in informing design and operation decisions during developing and implementing μCHP technologies in residential applications, especially single dwellings

Understanding energy efficiency in households and hotels in Spain: a combination of methods to account for stakeholders, views.

143 p.The growing complexities of the current energy price crisis and environmental problems are leading to an acceleration in reductions in energy consumption. Stimulating the adoption of energy efficiency is one of the strategies formulated by the international community to reduce energy consumption and greenhouse gas emissions. Buildings in the EU are responsible for 40% of our energy consumption and 36% of greenhouse gas emissions. Improving energy efficiency in buildings therefore plays a key role in attaining the ambitious goal of carbon-neutrality by 2050. Huge investments in energy efficiency are required to achieve energy savings and climate goals. However, despite its significant monetary benefits and environmental advantages, levels of EE in buildings are generally low. This is the so-called energy efficiency gap. Many reasons exist for it, which can be mainly grouped into market, behavioural and other failures. And different energy efficiency policy instruments can be used to address those failures. If energy efficiency leads to significant reductions in energy consumption (and bills), why do residential and non-residential buildings invest so little in it? How should policy makers encourage investments in energy efficiency? What effective ways are there of making energy efficiency policies effective and accepted by all stakeholders? By answering these overarching research questions, the dissertation¿s main goal is to study the effects of energy efficiency policies and to understand how these policy instruments can be designed to promote effective, cheaper reductions in emissions and energy consumption in households and hotels, mainly in the context of Spain. To that end, this dissertation integrates and combines different methodologies, i.e. semi-quantitative approaches through the use of focus groups and surveys to understand behavioural complexity; and a quantitative econometric approach based on hedonic price method to provide evidence of the effectiveness of EE labels. We find that the application of policy packages may be useful for less coercive policy instruments (especially for households) and for ambitious EE targets. Specifically, ambitious technical standards and specific regulation would ensure that energy is saved. Environmental education and information policies seem to be useful in helping consumers to make better decisions. Additionally, in the light of variation in policy acceptability for economic instruments, energy tax could be combined with subsidies or other revenue recycling schemes. Findings suggest that various policy instruments can be used to help achieve EE targets, but good policy design and excellent implementation are needed, considering behavioural complexity on the part of key stakeholders and features of the policy instrumentsbc3: basque center for climate chang

A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings

Buildings currently account for 30–40 percent of total global energy consumption. In particular, commercial buildings are responsible for about 12 percent of global energy use and 21 percent of the United States’ energy use, and the energy demand of this sector continues to grow faster than other sectors. This increasing rate therefore raises a critical concern about improving the energy performance of commercial buildings. Recently, researchers have investigated ways in which understanding and improving occupants’ energy-consuming behaviors could function as a cost-effective approach to decreasing commercial buildings’ energy demands. The objective of this paper is to present a detailed, up-to-date review of various algorithms, models, and techniques employed in the pursuit of understanding and improving occupants’ energy-use behaviors in commercial buildings. Previous related studies are introduced and three main approaches are identified: (1) monitoring occupant-specific energy consumption; (2) Simulating occupant energy consumption behavior; and (3) improving occupant energy consumption behavior. The first approach employs intrusive and non-intrusive load-monitoring techniques to estimate the energy use of individual occupants. The second approach models diverse characteristics related to occupants’ energy-consuming behaviors in order to assess and predict such characteristics’ impacts on the energy performance of commercial buildings; this approach mostly utilizes agent-based modeling techniques to simulate actions and interactions between occupants and their built environment. The third approach employs occupancy-focused interventions to change occupants’ energy-use characteristics. Based on the detailed review of each approach, critical issues and current gaps in knowledge in the existing literature are discussed, and directions for future research opportunities in this field are provided

"Ann" artifical neural networks and fuzzy logic models for cooling load prediction

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2005Includes bibliographical references (leaves: 44-45)Text in English; Abstract: Turkish and Englishx, 45 leavesIn this thesis Artificial Neural Networks (ANN) and fuzzy logic models of the building energy use predictions were created. Data collected from a Hawaian 42 storey commercial building chiller plant power consumption and independent hourly climate data were obtained from the National Climate Data Center of the USA. These data were used in both ANN and the fuzzy model setting up and testing. The tropical climate data consisted of dry bulb temperature, wet bulb temperature, dew point temperature, relative humidity percentage, wind speed and wind direction.Both input variables and the output variable of the central chiller plant power consumption were fuzzified, and fuzzy membership functions were employed. The Mamdani fuzzy rules (32 rule) in If .Then format with the centre of gravity (COG; centroid) defuzzification were employed. The average percentage error levels in the fuzzy model and the ANN model were end up with 11.6% (R2.0.88) and 10.3% (R2.0.87), respectively. The fuzzy model is successfully presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations that makes this fuzzy model

Distributed Energy Infrastructure Development: Geospatial & Economic Feasibility in Rural West Virginia

Energy transition from conventional to centralized power plants, including coal-fired units, is critical for West Virginia’s long-term energy and economic future. The socioeconomic downturn in West Virginia was deeply connected with the dependence on the centralized coal industry and the coal economy. Most traditional coal communities in rural West Virginia struggle to maintain economic viability, potentially leading to outmigrations and poor energy resilience. I investigated the possibility of introducing community-sized distributed energy systems in these rural communities to improve energy resilience and accommodate the future transition from centralized coal-generated energy. My goal was to identify rural regions where distributed energy can be utilized at an optimal cost, thus improving energy resiliency within these communities and positively impacting the economy. This study provided a geospatial modeling approach with Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS) suitability assessment to identify the feasible locations of small-scale distributed generation for wind, solar, and hydropower energies. The net value comparison analysis was conducted utilizing the levelized cost of energy (LCOE) and levelized avoided cost of energy (LACE) to determine the differences in investment costs for each distributed generation type compared with traditional coal-generated electricity. I expected the spatial analysis results to reveal optimal sites for the specific distributed energy types. I found that wind and solar distributed generation have stronger presences in southern and eastern West Virginia counties, while suitable small hydropower development locations are spread across the state. This study provided insight into future distributed energy and its infrastructure development possibilities in rural West Virginia