Showing papers in "Applied Energy in 2021"

TL;DR: In this article, the impacts and challenges of COVID-19 pandemics on energy demand and consumption and highlights energy-related lessons and emerging opportunities are discussed. But, although the overall energy demand declines, the spatial and temporal variations are complicated.

TL;DR: A comprehensive overview of recent advances in the P1P energy system and an insightful discussion of the challenges that need to be addressed in order to establish P2P sharing as a viable energy management option in today’s electricity market are focused on.

TL;DR: An in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted.

TL;DR: This paper reviews the application of machine learning techniques in building load prediction under the organization and logic of the machine learning, which is to perform tasks T using Performance measure P and based on learning from Experience E.

TL;DR: A new decentralized P2P energy trading platform that guarantees a near-optimally efficient market solution, preserves players’ privacy, and allows inter-temporal market products trading is developed.

TL;DR: This paper provides a review of the P2P energy trading that is necessary to understand the current approaches, challenges, and future research that should be conducted in this area.

TL;DR: In this article, the authors comprehensively reviewed the various deep learning technologies being used in wind power forecasting, including the stages of data processing, feature extraction, and relationship learning, and compared the forecasting performance of some popular models.

TL;DR: In this paper, the authors provide a comprehensive analysis of the dynamics between energy transition and COVID-19 around the world and propose a low-carbon energy transition roadmap in the post-pandemic era.

TL;DR: In this paper, a tool allowing modelling of complex energy system transition for power, heat, transport and industry sectors, responsible for over 75% of the CO2eq emissions, in full hourly resolution, is presented in this research and tested for the case of Kazakhstan.

TL;DR: Current trends in the field of energy system modelling are identified of increasing modelling of cross-sectoral synergies, growing focus on open access, and improved temporal detail to deal with planning future scenarios with high levels of variable renewable energy sources.

TL;DR: This paper performs a literature survey of state-of-the-art models, comparing state- of- the-art statistical and deep learning methods across multiple years and markets, and puts forward a set of best practices.

TL;DR: In this article, the authors investigated the global potential of green ammonia production from semi-flexible ammonia plants utilising a cost-optimised configuration of hybrid PV-wind power plants, as well as conversion and balancing technologies.

TL;DR: The results show that the proposed approach achieves higher accuracy than the standalone offline long short term memory network and five other online algorithms and the time to learn from new samples is only a fraction of the time needed to re-train the offline model.

TL;DR: In this paper, a wide range of alternative technologies have been assessed to determine the total cost of hydrogen production by coupling life-cycle assessments with an economic evaluation of the environmental externalities of production.

TL;DR: A deep neural network (DNN) based method is proposed to estimate SOC with only 10-min charging voltage and current data as the input, which enables fast and accurate SOC estimation with an error of less than 2.03% over the entire battery SOC range.

TL;DR: In this paper, the power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models.

TL;DR: Wang et al. as mentioned in this paper employed the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic.

TL;DR: Simulation results show that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

TL;DR: The five most studied types of ML algorithms for battery SOH estimation are systematically reviewed and it can be concluded that amongst these methods, support vector machine and artificial neural network algorithms are still research hotspots.

TL;DR: In this paper, the authors explored how the power sector would have to change in reaction to a tighter EU ETS target, and analyzed the technological and economic implications, and found that tightening the target would speed up the transformation by 3-17 years.

TL;DR: The findings show the far reaching implications of the COVID-19 crisis, and contribute to the understanding and planning of higher renewables share scenarios, which will become more prevalent in the battle against climate change.

TL;DR: In this article, the authors quantify the impacts of the COVID-19 lockdown on the energy consumption (electricity, hot water and space heating) in residential buildings by answering these two questions: (i) Did the lockdown lead to changes in total energy consumption? and (ii) Did changes in consumption patterns (i.e. time of the day at which energy is consumed)?

TL;DR: In this paper, a qualitative analysis is performed in a parallel approach from the key viewpoint of the renewable and sustainable energy transition, digital transformation of the energy sector and energy affordability in the post-COVID world.

TL;DR: In this article, a comprehensive state-of-the-art review of latent thermal energy storage (LTES) technology with a particular focus on medium-high temperature phase change materials for heat recovery, storage and utilisation is provided.

TL;DR: Combining econometric and artificial intelligence methods, the proposed model has an excellent performance on the current carbon price, with smaller errors than single econometrics or AI models or decomposition-ensemble models with linear simple superposition approaches.

TL;DR: It is concluded that this growth is not fully compensated by efficiency gains of data center technological innovations and the future impact of the data centers’ electricity consumption is vulnerable to behavioral usage trends.

TL;DR: A comprehensive review of energy harvesting technologies for different applications in the land transportation is presented in this article, where different applications and energy utilizations of the presented energy harvesting systems are summarized.

TL;DR: In this article, the assessment of energy flow, environmental emissions of walnut orchards in Alborz province of Iran and their simultaneous optimization by multi-objective imperialist competitive algorithm are the main goals of this investigation.

TL;DR: A novel framework incorporating the concepts of transfer learning and network pruning is proposed to build compact Convolutional Neural Network models on a relatively small dataset with improved estimation performance, which outperforms other models in terms of accuracy and computational efficiency.

TL;DR: In this paper, a review of the works about stabilizing techniques for hydrated salt phase change materials (PCMs) is presented, and the analysis around the influence of stabilizing method on the thermophysical properties of form-stable salt PCMs is presented.