Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector

Advanced metering infrastructure (AMI) is becoming increasingly popular as an efficient means of energy demand management. By collecting energy data through AMI, it is possible to provide users with information that can induce them to change their behavior. To ensure that AMI continues to expand and to encourage the use of energy data, it is important to increase consumer participation and analyze their preferred service attributes. This study utilized a choice experiment to analyze consumer preferences for and acceptance of smart energy services based on AMI data. The results of a mixed logit model estimation show that consumers prefer the electricity information service for individual households and the social safety-net service among convergence services. A scenario analysis confirms that monetary compensation to offset any additional charges is important to maintain the level of consumer acceptance. These empirical findings offer insights for policymakers and companies seeking to develop policies and similar services.

Energy Prediction under Changed Demand Conditions: Robust Machine Learning Models and Input Feature Combinations

Deciding on a suitable algorithm for energy demand prediction in a building is non-trivial and depends on the availability of data. In this paper we compare four machine learning models, commonly found in the literature, in terms of their generalization performance and in terms of how using different sets of input features affects accuracy. This is tested on a data set where consumption patterns differ significantly between training and evaluation because of the Covid-19 pandemic. We provide a hands-on guide and supply a Python framework for building operators to adapt and use in their applications. © International Building Performance Simulation Association, 2022

A Hybrid Deep Neural Network Architecture for Day-Ahead Electricity Forecasting: Post-COVID Paradigm

Predicting energy demand in adverse scenarios, such as the COVID-19 pandemic, is critical to ensure the supply of electricity and the operation of essential services in metropolitan regions. In this paper, we propose a deep learning model to predict the demand for the next day using the "IEEE DataPort Competition Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm” database. The best model uses hybrid deep neural network architecture (convolutional network–recurrent network) to extract spatial-temporal features from the input data. A preliminary analysis of the input data was performed, excluding anomalous variables. A sliding window was applied for importing the data into the network input. The input data was normalized, using a higher weight for the demand variable. The proposed model's performance was better than the models that stood out in the competition, with a mean absolute error of 2361.84 kW. The high similarity between the actual demand curve and the predicted demand curve evidences the efficiency of the application of deep networks compared with the classical methods applied by other authors. In the pandemic scenario, the applied technique proved to be the best strategy to predict demand for the next day.

The sustainable city

This chapter sets out the path of the UN sustainable development goals and looks at the universal challenges and the irrefutable truths that numbers bring to all cities. It explores the role for cities in that process and identifies the levers city leaders need from their governments and the international community to tackle climate change and its wider implications, in cities, and globally. The places with some of the fastest pace of energy demand are also those countries experiencing the fastest rates of urbanization. COVID-19 showed that numbers matter not only in the tragedy it inflicted, but in the extraordinary solidarity and unexpected places where this shared human tragedy was manifested. Cities and local governments are marginalized to the 'ante-theatre' of global discussions and decisions on the world stage. Cities and local governments have a rightful and critical place in shaping our future-for people living in urban areas, and for everyone. © 2022 John Wiley & Sons Ltd. All rights reserved.

Energy system analysis with a focus on future energy demand projections: The case of Norway

Post Covid-19 pandemic and the Ukrainian war are significantly impacting energy systems worldwide, faltering investments and threatening to throttle the expansion of primary clean energy technologies, even in the case of a well-structured and managed energy system, such as Norway. This unprecedented crisis requires deeper analyses and well-measured actions from the main actors in Norway's energy and climate sector. Hence, providing and highlighting needed interventions and improvements in the energy system is crucial. This study analyzes demand-side energy in Norway's households, industry, transport, and "other” sectors. LEAP model, a powerful energy system analysis tool, was used to conduct the analysis based on Baseline and Mitigation scenarios. The energy demand by sector and fuel type toward 2050 is forecasted, firstly by considering a set of parameters and key assumptions that impact the security of supply and secondly on the ambitious target of Norway's government in decreasing GHG emissions by 55% in 2030 and 90–95% by the year 2050 compared to 1990 levels. The mitigation scenario aims to diversify the overall national energy system and technological changes based on large-scale renewable energy sources (RES) integration. From the perspective of climate change issues, EV's include an attractive option for deep decarbonization, including other sustainable fuel sources such as H2, biofuel mixed with diesel, the use of excess heat deriving from industry to cover households' heating demand, and integration of large-scale heat pumps driven by RES during off-peak demand is applied. Energy demand projections are uncertain, and the main goal is to show how different scenario projections up to 2050 affect the whole of Norway's energy system, leading to a combined global warming potential (GWP) of around 7.30 MtCO2 in the mitigation scenario from 56.40 MtCO2 tones released in the baseline scenario, by reaching only 77.5% reduction referring to 1990 level. This study's findings show that the net-zero ambitions by the end of 2050 are impossible without the carbon tax application and carbon capture storage (CCS), especially in the oil and gas industry. © 2023 The Authors

Social practices and energy use at home during the first Italian lockdown due to Covid-19.

During the beginning of the Covid-19 pandemic, cities became large residential consumers of energy. In general, energy demand has decreased, but the users who used the most energy during the pandemic were the people in their homes creating a change compared to the past. How have household habits changed affecting energy use during the lockdown? Has energy demand changed equally in all homes? What factors help explain the change in daily household habits and the change in energy use? Via distribution of a questionnaire completed by 3519 people living in Italy during the first lockdown #StayAtHome, the change in daily habits and consequent energy use were investigated. It collected data on socio-demographic and household characteristics and the material context in which people live. The results were interpreted according to the social practice approach that has been used in the past to analyse energy habits and use of households, for example, for cooking. The results can support the interpretation of energy demand studies in the pandemic period and address decisions and policymaking for sustainable energy transition.

The Covid-19 Total Lockdown Effects on Grid Energy Demand: A Malaysia Experience

The Covid-19 disease is a highly contagious disease that causes respiratory tracts and lung infections, where some of the cases can lead to fatalities. Malaysia recorded the first Covid-19 case on January 25th, 2020. Although the government at the time considered the disease was not a threat, as the days went by the cases started to increase. The total lockdown measure, also known as the movement control order (MCO) was declared by the new Government of Malaysia following the surge of Covid-19 cases in early March 2020. The new prime minister, following a change of government, announced that schools were to be closed, offices and non-essential business activities were told to cease operation, and people were ordered to stay in their homes. As a newly industrialized country, with little economic activities, there were significant drops in the energy demand in Malaysia. This paper analyzes the change in the power demand and energy consumption trend in Malaysia before, during and after the Covid-19 total lockdown. © 2022 IEEE.

The daily price and income elasticity of natural gas demand in Europe

Data from 15 European countries is analysed to provide novel estimates of daily own-price, cross-price and income elasticities of natural-gas-demand from 2016 to 2020. The results show that: first, there is a strong-seasonal component in the October–February period during which residential-demand has a higher share on total demand, and gas price is not a determinant factor for most of the countries. This seasonal profile makes price-based tools more effective modifying end-consumer behaviours from March to August when estimated own-price elasticities present larger values in absolute terms. Second, there are estimated positive own-price elasticities from October to February in Bulgaria, Luxemburg, Poland, the UK, and Portugal. The first four countries present natural gas prices below the EU-28 average during the analysed period and it is argued that positive elasticities may reflect a disconnection between the price traded on the organized markets and the real price perceived by end-customers. For Portugal, who is currently carrying out a very aggressive policy to become coal-free by the end of 2021, natural gas and coal are mainly consumed in power sector to provide flexibility and back up renewable generation. The limited alternatives to provide these services may explain why coal and natural gas are found to be complementary. Finally, it is found that lockdowns due to covid-19 highly impacted on natural gas demand, confirming for the first time in the literature a “double heating effect”. Our results help to find when price-based tools by policymakers will influence more effectively natural-gas-demand following economic and environmental goals. © 2022 The Authors

Fostering Energy Resilience in the Rural Thai Power System—A Case Study in Nakhon Phanom

With rising electricity demand, heavy reliance on imports, and recent economic downturns due to the negative impact of the COVID-19 pandemic, supply chain bottlenecks, and the Russian invasion of Ukraine, Thailand is suffering severely from energy resilience risks. The government has therefore set a goal of decentralizing energy production through small-scale distributed renewable energy systems. To support their design and the planning process, we simulate multiple scenarios with wind turbines, photovoltaic systems, and battery storage for a model community in rural Nakhon Phanom, Thailand. Using the software NESSI4D, we evaluate and discuss their impact on energy resilience by considering environmental sustainability, economic attractiveness, and independence from the central power grid. To fill the gap of missing data on energy demand, we synthesize high-resolution load profiles from the Thailand Vietnam Socio-Economic Panel. We conclude that distributed photovoltaic systems with additional battery storage are only suitable to promote energy resilience if the government provides appropriate financial incentives. Considering temporal variations and local conditions, as well as a participatory decision-making process, are crucial for the long-term success of energy projects. Our advice to decision-makers is to design policies and regulatory support that are aligned with the preferences and needs of target communities.

Residential geothermal air-conditioning: inhabitants’ comfort, behaviour and energy use

How do occupants’ behaviour and expectations influence energy use for residential geothermal air-conditioning? This detailed study of 40 homes with geothermal AC in Sydney, Australia, during the period 2019–20 covers one of hottest Australian summers and increased daytime occupancy over the winter due to working from home during Covid-19 lockdowns. Monitored data are analysed for ground source heat pump (GSHP)-AC energy, occupancy, indoor conditions, as well as a snapshot resident feedback captured during hot and cold spells. Findings indicate that the homes built to comply with 2011 energy efficiency standards maintain indoor temperatures within 10–12°C of peak summer and minimum winter temperatures, without AC. A general preference to adopt adaptive strategies such as ceiling fans and appropriate clothing before deploying AC is evident for moderately hot and cold days. A heightened dependence on AC is seen for extreme days. However, a significant number of houses adhere to a narrow range of acceptable temperatures, thereby increasing the take-up of GSHP-AC and energy consumption. The replacement of conventional AC with alternate technologies is not a one-stop solution in, and of, itself. There is a need for improved building low energy design and construction based on a better understanding of occupant behaviour and energy consequences. POLICY RELEVANCE Although geothermal systems offer potential energy savings (especially in extreme conditions) and their potential for seamless technological replacement of conventional AC in homes, the findings suggest GSHP-AC is not a one-stop solution to reduce dependence on AC. The study reveals that the unconstrained use of GSHP-AC can increase energy consumption and squander energy savings achieved through its technological efficiency. The ‘conditioned’ expectations of inhabitants, stimulated by a lower tolerance of ‘imperfect’ conditions and availability of heating and cooling on standby, can lead to increased dependence and usage. In an increasing warming world, more stringent guidelines are needed for thermal performance and design to mitigate residual discomfort and transform occupant practices. These must also be supported with occupant education and engagement to ensure the design intent is realised. © 2022 The Author(s).

Investigation of energy demand correlation during pandemic using self-organizing map algorithm

The world faces a significant impact from the coronavirus disease 2019 (Covid-19) pandemic, which also influences energy consumption. This study investigates the substantial connection of the classified data between power consumption, cooling degree days, average temperature, and covid-19 cases information using mathematical and neural network approaches regression analysis, and self-organizing maps. It is well established that various data mining methods have revamped the classification process of data analytics. Specifically, this study investigates the correlation between the collected variables using regression analysis and selecting the best-matching unit under the normalization method using self-organizing maps. The self-organizing maps become better when the datasets have variations;the result denotes that this method produced high mapping quality based on the map size and normalization method. Furthermore, the data crossing connection is indicated using the regression analysis method. Finally, the classified data results during the movement control order are validated in self-organizing maps to achieve the study objective. By performing these methods, this study established that the correlation between the energy demand towards cooling degree days, average temperature, and covid-19 cases is very weak. The verification has been made where the ‘logistic’ normalization method has produced the best classification result. © 2022, Institute of Advanced Engineering and Science. All rights reserved.

Pathways to Carbon-Free Transport in Germany until 2050

The transport sector has to be widely decarbonized by 2050 to reach the targets of the Paris Agreement. This can be performed with different drive trains and energy carriers. This paper explored four pathways to a carbon-free transport sector in Germany in 2050 with foci on electricity, hydrogen, synthetic methane, or liquid synthetic fuels. We used a transport demand model for future vehicle use and a simulation model for the determination of alternative fuel vehicle market shares. We found a large share of electric vehicles in all scenarios, even in the scenarios with a focus on other fuels. In all scenarios, the final energy consumption decreased significantly, most strongly when the focus was on electricity and almost one-third lower in primary energy demand compared with the other scenarios. A further decrease of energy demand is possible with an even faster adoption of electric vehicles, yet fuel cost then has to be even higher or electricity prices lower.

Price and income elasticity of natural gas demand in Europe and the effects of lockdowns due to Covid-19

We analyse a panel of 25 European-countries to provide novel estimates of monthly own-price, cross-price, and income elasticities of natural-gas-demand from 2005 to 2020. We find that: first, there is an European Standard Behaviour (ESB) with a strong-seasonal component. Second, we identify three different patterns from the ESB: 1) France, Denmark and Estonia present slightly positive elasticities in the short-run and a lack of sensitivity to own-price variations in the long-run –we argue this phenomenon is due to a higher weight of heating demand-. 2) Latvia presents a lower sensitivity to own-price variations than the ESB -we argue due to the role of natural gas as a unique backup technology in the power sector-. 3) In Portugal, natural gas showed the highest own-price elasticities – we argue that natural gas is used mainly in the power sector with substitutive technologies-. Finally, we find that Covid-19-lockdowns highly impacted natural-gas-demand, confirming the “double-heating-effect”.

Implications of poorly designed climate policy on energy poverty: Global reflections on the current surge in energy prices

With the recovery of the world economy following the easing of restrictions designed to contain COVID-19, energy demand has surged, even as natural gas stocks have run dangerously low. In addition to the reduced investment in upstream energy resources, infrastructure and maintenance since the outbreak of COVID-19, the situation is exacerbated by supply constraints. This situation has triggered one of the first significant energy shocks of the green era and exposed the fragilities of the premature greening of energy system processes. A recent study indicates that 80 million European households are struggling to stay warm, and the recent spike in energy costs is expected to aggravate the problem. Here, we examine the impact of the energy-price boom on the state of energy poverty in Europe. This paper highlights how energy prices and the green transition may exacerbate the energy poverty trap in Europe. It emphasizes one of the downside effects of poorly designed climate policies. This discussion offers insights and important policy implications that may help trigger debates on energy poverty in developing countries. It argues that poorly designed climate policy may initiate new forms of inequalities and reaffirm existing ones by undermining the foundation of individuals' capabilities. It also reveals a pivotal shift in the underlying drivers of countries' future success in addressing climate change, eliminating energy poverty, and achieving energy justice. The analysis strengthens the case that climate policies must go hand-in-hand with inequality and energy poverty mitigation.

Space temperature policy towards net-zero: recommendations from a systematic review of UK HEI heating policies

The UK government has committed to reducing its carbon emissions to net-zero by 2050. Higher education institutions (HEIs) are high energy users, with the largest proportion of their energy demand for space heating;an area still dominated by carbon-intensive fuels. This research addresses the UK HEI space temperature policy landscape, making direct links between space temperature policy and carbon management, advocating the development of evidence-based policies as a critical tool for reducing carbon emissions within the sector. Sixty-six space temperature policies were reviewed, and five experienced energy managers were interviewed to understand the range, development and use of space temperature policies in UK HEIs. The research identified a lack of consistency across these policies, leading to missed opportunities for making energy and carbon savings. The research highlights gaps in the available data and literature needed to connect policy to its effectiveness, and identifies the use of policy as a defensive tool against complaints rather than an active driver of energy reduction. A series of recommendations are proposed for national and institutional policymakers, suggesting areas for improvement and future research to facilitate effective development and practice in space temperature policy towards net-zero.

Forecasting China's energy demand post-COVID-19 pandemic: Insights from energy type differences and regional differences

As the first country to restart the economy after the COVID-19 pandemic, China's fast-growing energy consumption has brought huge challenges to the energy system. In this context, ensuring a stable energy supply requires accurate estimates of energy consumption for China's post-Covid-19 pandemic economic recovery. To this end, this study uses multiple panel regression model to explore the relationship between energy consumption and economic growth from the perspective of energy sources (total energy, coal, oil, natural gas) and regional difference. The data from 30 provinces in China from 2000 to 2017 were selected. Our findings indicate that China economic growth has led to the largest increase for oil consumption, followed by natural gas consumption, and finally coal consumption. That is, China economic growth has led to the largest increase for oil consumption, followed by natural gas consumption, and finally coal consumption. In addition, the coefficients of regional energy consumption equations are heterogeneous. Among them, energy consumption growth in provinces with high energy consumption is most affected by economic growth, followed by provinces with low energy consumption, and finally provinces with middle energy consumption. © 2022 The Author(s)

COVID-19 and EU Climate Targets: Can We Now Go Further?

This paper examines the implications of the COVID-19 crisis on the 2030 EU CO2 emissions target, considering a range of economic growth scenarios. With lower economic activity resulting from the COVID-19 crisis, we find that existing climate policy measures could overshoot the current 40% EU target in 2030. If policymakers consequently relax climate policy measures to maintain the 2030 target, the opportunity will be missed to align EU climate policy with longer-term Paris emissions mitigation goals. Our analysis highlights that although existing climate policy measures will likely reduce emissions more than 40% by 2030 in the wake of the pandemic, they will not be enough to meet the Paris agreement. More stringent measures, such as those proposed under the Green New Deal, will still be needed and may be less costly than previously estimated.

Data-driven load profile modelling for advanced measurement and verification (M&V) in a fully electrified building

The process of decarbonising stock will result in a considerable shift in consumption away from fossil fuels and toward electricity. The growing trend of building electrification necessitates a thorough examination from the standpoint of end-use efficiency and dynamic behaviour in order to fully understand the potential for grid flexibility. The problem of accurately representing dynamic behaviour (e.g. electric load profiles) while retaining simple and easy to use modelling approaches (i.e. supporting a “human in the loop” approach to data-driven methodologies) is a challenging task, especially when operating conditions are very variable. For these reasons, we used an interpretable (regression-based) technique called Time Of Week a Temperature (TOWT) to predict the dynamic electric load profiles before, during, and after the COVID lockdown (for nearly 4 years) of a public office building in Southern Italy, the Procida City Hall. TWOT models perform reasonably well in most conditions, and their application allowed for the detection of changes in energy demand patterns, critical aspects to consider when tuning them, and areas for improvement in algorithmic formulation and data visualisation, which will be the focus of future research.

Vehicle Model-Based Driving Strategy Optimization for Lightweight Vehicle

In this paper, driving strategy optimization for a track is proposed for an energy efficient battery electric vehicle dedicated to the Shell Eco-marathon. A measurement-based mathematical vehicle model was developed to simulate the behavior of the vehicle. The model contains complicated elements such as the vehicle’s cornering resistance and the efficiency field of the entire powertrain. The validation of the model was presented by using the collected telemetry data from the 2019 Shell Eco-marathon competition in London (UK). The evaluation of applicable powertrains was carried out before the driving strategy optimization. The optimal acceleration curve for each investigated powertrain was defined. Using the proper powertrain is a crucial part of energy efficiency, as the drive has the most significant energy demand among all components. Two tracks with different characteristics were analyzed to show the efficiency of the proposed optimization method. The optimization results are compared to the reference method from the literature. The results of this study provide an applicable vehicle modelling methodology with efficient optimization framework, which demonstrates 5.5% improvement in energy consumption compared to the reference optimization theory.

The wide range of possible aviation demand futures after the COVID-19 pandemic

Aviation has been identified as one of the crucial hard-to-abate sectors, as long-range aviation in particular will continue to depend on liquid fuels for the foreseeable future. The sector was also one of the fastest growing emitters of fossil CO2 emissions until 2019 but experienced sharply reduced demand during the COVID-19 pandemic, making future demand outlooks more uncertain. While past studies have looked at the variation in future aviation demands due to variations in demographics, income levels, and pricing policies, an exploration of potentially more sustainable demand futures does not yet exist. Here we use an open-source model with a detailed representation of country-level aviation demand per international/domestic and business/leisure segments to analyze a range of scenarios based on a consistent and comprehensive interpretation of the qualitative narratives related to behavioural aspects as well as the socioeconomic data from different shared socioeconomic pathways (SSPs). Our results show a potential stabilization of global aviation demand at roughly twice the 2019 level in an SSP1 scenario, a weakened growth for an SSP2 scenario, while an SSP5 scenario projects an aviation future virtually unaffected by the COVID-19 shock, resulting in continued high growth rates. Further results show that without specific interventions that change the past demand growth patterns, the aviation sector could grow to levels that are very challenging to defossilize in a sustainable manner. Therefore, policies aiming at less frequent flying seem to be an important component of long-term decarbonisation strategies, and decisions regarding airport extensions should carefully assess the risk of stranded infrastructure.