Showing papers in "Energies in 2018"

Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †

Abstract: Background: With the development of smart grids, accurate electric load forecasting has become increasingly important as it can help power companies in better load scheduling and reduce excessive electricity production. However, developing and selecting accurate time series models is a challenging task as this requires training several different models for selecting the best amongst them along with substantial feature engineering to derive informative features and finding optimal time lags, a commonly used input features for time series models. Methods: Our approach uses machine learning and a long short-term memory (LSTM)-based neural network with various configurations to construct forecasting models for short to medium term aggregate load forecasting. The research solves above mentioned problems by training several linear and non-linear machine learning algorithms and picking the best as baseline, choosing best features using wrapper and embedded feature selection methods and finally using genetic algorithm (GA) to find optimal time lags and number of layers for LSTM model predictive performance optimization. Results: Using France metropolitan’s electricity consumption data as a case study, obtained results show that LSTM based model has shown high accuracy then machine learning model that is optimized with hyperparameter tuning. Using the best features, optimal lags, layers and training various LSTM configurations further improved forecasting accuracy. Conclusions: A LSTM model using only optimally selected time lagged features captured all the characteristics of complex time series and showed decreased Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for medium to long range forecasting for a wider metropolitan area.

Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities

Abstract: In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted.

Biofuels production by biomass gasification: A review

Abstract: The production of biofuels from renewable sources is a major challenge in research. Methanol, ethanol, dimethyl ether (DME), synthetic natural gas (SNG), and hydrogen can be produced from syngas which is the result of the gasification of biomasses. Syngas composition varies according to the gasification technology used (such as fixed bed reactors, fluidized bed reactors, entrained flow reactors), the feedstock characteristics, and the operating parameters. This paper presents a review of the predominant biomass gasification technologies and biofuels obtained from syngas by biomass gasification.

AC Ship Microgrids: Control and power management optimization

Abstract: At sea, the electrical power system of a ship can be considered as an islanded microgrid. When connected to shore power at berth, the same power system acts as a grid connected microgrid or an extension of the grid. Therefore, ship microgrids show some resemblance to terrestrial microgrids. Nevertheless, due to the presence of large dynamic loads, such as electric propulsion loads, keeping the voltage and frequency within a permissible range and ensuring the continuity of supply are more challenging in ship microgrids. Moreover, with the growing demand for emission reductions and fuel efficiency improvements, alternative energy sources and energy storage technologies are becoming popular in ship microgrids. In this context, the integration of multiple energy sources and storage systems in ship microgrids requires an efficient power management system (PMS). These challenging environments and trends demand advanced control and power management solutions that are customized for ship microgrids. This paper presents a review on recent developments of control technologies and power management strategies proposed for AC ship microgrids.

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

Abstract: Power systems are the most complex systems that have been created by men in history To operate such systems in a stable mode, several control loops are needed Voltage frequency plays a vital role in power systems which need to be properly controlled To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance Likewise, the power exchanges between different control areas are controlled by LFC approaches In recent decades, many control approaches have been suggested for LFC in power systems This paper presents a comprehensive literature survey on the topic of LFC In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC

Significance and Challenges of Biomass as a Suitable Feedstock for Bioenergy and Biochemical Production: A Review

Abstract: Fossil fuels have been a major contributor to greenhouse gases, the amounts of which could be reduced if biofuels such as bioethanol and biodiesel were used for transportation. One of the most promising biofuels is ethyl alcohol. In 2015, the world production of ethanol was 25.6 billion gallons and the USA, Brazil, China, the European Union, and 28 other countries have set targets for blending ethanol with gasoline. The two major bio-source materials used for ethanol production are corn and sugarcane. For 1st generation biofuels, sugarcane and corn feedstocks are not able to fulfill the current demand for alcohol. Non-edible lignocellulosic biomass is an alternative bio-source for creating 2nd generation biofuels and algae biomass for 3rd and 4th generation biofuels. This review discusses the significance of biomass for the different generations of biofuels, and biochemical and thermochemical processes, and the significance of biorefinery products.

A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation

Abstract: The solar photovoltaic (PV) energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX) neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network

Abstract: Accurate electrical load forecasting is of great significance to help power companies in better scheduling and efficient management. Since high levels of uncertainties exist in the load time series, it is a challenging task to make accurate short-term load forecast (STLF). In recent years, deep learning approaches provide better performance to predict electrical load in real world cases. The convolutional neural network (CNN) can extract the local trend and capture the same pattern, and the long short-term memory (LSTM) is proposed to learn the relationship in time steps. In this paper, a new deep neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy. The proposed model was tested in a real-world case, and detailed experiments were conducted to validate its practicality and stability. The forecasting performance of the proposed model was compared with the LSTM model and the CNN model. The Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were used as the evaluation indexes. The experimental results demonstrate that the proposed model can achieve better and stable performance in STLF.

Impact of Electric Vehicle Charging Station Load on Distribution Network

Abstract: Recent concerns about environmental pollution and escalating energy consumption accompanied by the advancements in battery technology have initiated the electrification of the transportation sector. With the universal resurgence of Electric Vehicles (EVs) the adverse impact of the EV charging loads on the operating parameters of the power system has been noticed. The detrimental impact of EV charging station loads on the electricity distribution network cannot be neglected. The high charging loads of the fast charging stations results in increased peak load demand, reduced reserve margins, voltage instability, and reliability problems. Further, the penalty paid by the utility for the degrading performance of the power system cannot be neglected. This work aims to investigate the impact of the EV charging station loads on the voltage stability, power losses, reliability indices, as well as economic losses of the distribution network. The entire analysis is performed on the IEEE 33 bus test system representing a standard radial distribution network for six different cases of EV charging station placement. It is observed that the system can withstand placement of fast charging stations at the strong buses up to a certain level, but the placement of fast charging stations at the weak buses of the system hampers the smooth operation of the power system. Further, a strategy for the placement of the EV charging stations on the distribution network is proposed based on a novel Voltage stability, Reliability, and Power loss (VRP) index. The results obtained indicate the efficacy of the VRP index.

A cost-effective decentralized control for AC-stacked photovoltaic inverters

Abstract: For an AC-stacked photovoltaic (PV) inverter system with N cascaded inverters, existing control methods require at least N communication links to acquire the grid synchronization signal. In this paper, a novel decentralized control is proposed. For N inverters, only one inverter nearest the point of common coupling (PCC) needs a communication link to acquire the grid voltage phase and all other N − 1 inverters use only local measured information to achieved fully decentralized local control. Specifically, one inverter with a communication link utilizes the grid voltage phase and adopts current control mode to achieve a required power factor (PF). All other inverters need only local information without communication links and adopt voltage control mode to achieve maximum power point tracking (MPPT) and self-synchronization with grid voltage. Compared with existing methods, the communication link and complexity is greatly reduced, thus improved reliability and reduced communication costs are achieved. The effectiveness of the proposed control is verified by simulation tests.

Role and Potential of Direct Interspecies Electron Transfer in Anaerobic Digestion

Abstract: Anaerobic digestion (AD) is an effective biological treatment for stabilizing organic compounds in waste/wastewater and in simultaneously producing biogas. However, it is often limited by the slow reaction rates of different microorganisms’ syntrophic biological metabolisms. Stable and fast interspecies electron transfer (IET) between volatile fatty acid-oxidizing bacteria and hydrogenotrophic methanogens is crucial for efficient methanogenesis. In this syntrophic interaction, electrons are exchanged via redox mediators such as hydrogen and formate. Recently, direct IET (DIET) has been revealed as an important IET route for AD. Microorganisms undergoing DIET form interspecies electrical connections via membrane-associated cytochromes and conductive pili; thus, redox mediators are not required for electron exchange. This indicates that DIET is more thermodynamically favorable than indirect IET. Recent studies have shown that conductive materials (e.g., iron oxides, activated carbon, biochar, and carbon fibers) can mediate direct electrical connections for DIET. Microorganisms attach to conductive materials’ surfaces or vice versa according to particle size, and form conductive biofilms or aggregates. Different conductive materials promote DIET and improve AD performance in digesters treating different feedstocks, potentially suggesting a new approach to enhancing AD performance. This review discusses the role and potential of DIET in methanogenic systems, especially with conductive materials for promoting DIET.

Biodiesel Production from Palm Oil, Its By-Products, and Mill Effluent: A Review

Abstract: The sustainability of petroleum-based fuel supply has gained broad attention from the global community due to the increase of usage in various sectors, depletion of petroleum resources, and uncertain around crude oil market prices. Additionally, environmental problems have also arisen from the increasing emissions of harmful pollutants and greenhouse gases. Therefore, the use of clean energy sources including biodiesel is crucial. Biodiesel is mainly produced from unlimited natural resources through a transesterification process. It presents various advantages over petro-diesel; for instance, it is non-toxic, biodegradable, and contains less air pollutant per net energy produced with low sulphur and aromatic content, apart from being safe. Considering the importance of this topic, this paper focuses on the use of palm oil, its by-products, and mill effluent for biodiesel production. Palm oil is known as an excellent raw material because biodiesel has similar properties to the regular petro-diesel. Due to the debate on the usage of palm oil as food versus fuel, extensive studies have been conducted to utilise its by-products and mill effluent as raw materials. This paper also discusses the properties of biodiesel, the difference between palm-biodiesel and other biodiesel sources, and the feasibility of using palm oil as a primary source for future alternative and sustainable energy sources.

A High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting

Abstract: One of the most important research topics in smart grid technology is load forecasting, because accuracy of load forecasting highly influences reliability of the smart grid systems. In the past, load forecasting was obtained by traditional analysis techniques such as time series analysis and linear regression. Since the load forecast focuses on aggregated electricity consumption patterns, researchers have recently integrated deep learning approaches with machine learning techniques. In this study, an accurate deep neural network algorithm for short-term load forecasting (STLF) is introduced. The forecasting performance of proposed algorithm is compared with performances of five artificial intelligence algorithms that are commonly used in load forecasting. The Mean Absolute Percentage Error (MAPE) and Cumulative Variation of Root Mean Square Error (CV-RMSE) are used as accuracy evaluation indexes. The experiment results show that MAPE and CV-RMSE of proposed algorithm are 9.77% and 11.66%, respectively, displaying very high forecasting accuracy.

An overview of recent developments in biomass pyrolysis technologies

Abstract: Biomass is a promising sustainable and renewable energy source, due to its high diversity of sources, and as it is profusely obtainable everywhere in the world. It is the third most important fuel source used to generate electricity and for thermal applications, as 50% of the global population depends on biomass. The increase in availability and technological developments of recent years allow the use of biomass as a renewable energy source with low levels of emissions and environmental impacts. Biomass energy can be in the forms of biogas, bio-liquid, and bio-solid fuels. It can be used to replace fossil fuels in the power and transportation sectors. This paper critically reviews the facts and prospects of biomass, the pyrolysis process to obtain bio-oil, the impact of different pyrolysis technology (for example, temperature and speed of pyrolysis process), and the impact of various reactors. The paper also discusses different pyrolysis products, their yields, and factors affecting biomass products, including the present status of the pyrolysis process and future challenges. This study concluded that the characteristics of pyrolysis products depend on the biomass used, and what the pyrolysis product, such as bio-oil, can contribute to the local economy. Finally, more research, along with government subsidies and technology transfer, is needed to tackle the future challenges of the development of pyrolysis technology.

Li-Ion Battery Fire Hazards and Safety Strategies

Abstract: In the past five years, there have been numerous cases of Li-ion battery fires and explosions, resulting in property damage and bodily injuries. This paper discusses the thermal runaway mechanism and presents various thermal runaway mitigation approaches, including separators, flame retardants, and safety vents. The paper then overviews measures for extinguishing fires, and concludes with a set of recommendations for future research and development.

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

Abstract: Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

Continuous Hydrothermal Liquefaction of Biomass: A Critical Review

Abstract: Hydrothermal liquefaction (HTL) of biomass is emerging as an effective technology to efficiently valorize different types of (wet) biomass feedstocks, ranging from lignocellulosics to algae and organic wastes. Significant research into HTL has been conducted in batch systems, which has provided a fundamental understanding of the different process conditions and the behavior of different biomass. The next step towards continuous plants, which are prerequisites for an industrial implementation of the process, has been significantly less explored. In order to facilitate a more focused future development, this review—based on the sources available in the open literature—intends to present the state of the art in the field of continuous HTL as well as to suggest means of interpretation of data from such plants. This contributes to a more holistic understanding of causes and effects, aiding next generation designs as well as pinpointing research focus. Additionally, the documented experiences in upgrading by catalytic hydrotreating are reported. The study reveals some interesting features in terms of energy densification versus the yield of different classes of feedstocks, indicating that some global limitations exist irrespective of processing implementations. Finally, techno-economic considerations, observations and remarks for future studies are presented.

A Review on Mechanisms for Piezoelectric-Based Energy Harvesters

Abstract: From last few decades, piezoelectric materials have played a vital role as a mechanism of energy harvesting, as they have the tendency to absorb energy from the environment and transform it to electrical energy that can be used to drive electronic devices directly or indirectly. The power of electronic circuits has been cut down to nano or micro watts, which leads towards the development of self-designed piezoelectric transducers that can overcome power generation problems and can be self-powered. Moreover, piezoelectric energy harvesters (PEHs) can reduce the need for batteries, resulting in optimization of the weight of structures. These mechanisms are of great interest for many researchers, as piezoelectric transducers are capable of generating electric voltage in response to thermal, electrical, mechanical and electromagnetic input. In this review paper, Fluid Structure Interaction-based, human-based, and vibration-based energy harvesting mechanisms were studied. Moreover, qualitative and quantitative analysis of existing PEH mechanisms has been carried out.

A Review of Sludge-to-Energy Recovery Methods

Abstract: The increasing volume of sewage sludge from wastewater treatment facilities is becoming a prominent concern globally. The disposal of this sludge is particularly challenging and poses severe environmental hazards due to the high content of organic, toxic and heavy metal pollutants among its constituents. This study presents a simple review of four sewage to energy recovery routes (anaerobic digestion, combustion, pyrolysis and gasification) with emphasis on recent developments in research, as well as benefits and limitations of the technology for ensuring cost and environmentally viable sewage to energy pathway. This study focusses on the review of various commercially viable sludge conversion processes and technologies used for energy recovery from sewage sludge. This was done via in-depth process descriptions gathered from literatures and simplified schematic depiction of such energy recovery processes when utilised for sludge. Specifically, the impact of fuel properties and its effect on the recovery process were discussed to indicate the current challenges and recent scientific research undertaken to resolve these challenges and improve the operational, environmental and cost competitiveness of these technologies.

Ocean Wave Energy Converters: Status and Challenges

Abstract: Wave energy is substantial as a resource, and its potential to significantly contribute to the existing energy mix has been identified. However, the commercial utilization of wave energy is still very low. This paper reviewed the background of wave energy harvesting technology, its evolution, and the present status of the industry. By covering the theoretical formulations, wave resource characterization methods, hydrodynamics of wave interaction with the wave energy converter, and the power take-off and electrical systems, different challenges were identified and discussed. Solutions were suggested while discussing the challenges in order to increase awareness and investment in wave energy industry as a whole.

Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

Abstract: 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 management.

Local Flexibility Market Design for Aggregators Providing Multiple Flexibility Services at Distribution Network Level

Abstract: This paper presents a general description of local flexibility markets as a market-based management mechanism for aggregators. The high penetration of distributed energy resources introduces new flexibility services like prosumer or community self-balancing, congestion management and time-of-use optimization. This work is focused on the flexibility framework to enable multiple participants to compete for selling or buying flexibility. In this framework, the aggregator acts as a local market operator and supervises flexibility transactions of the local energy community. Local market participation is voluntary. Potential flexibility stakeholders are the distribution system operator, the balance responsible party and end-users themselves. Flexibility is sold by means of loads, generators, storage units and electric vehicles. Finally, this paper presents needed interactions between all local market stakeholders, the corresponding inputs and outputs of local market operation algorithms from participants and a case study to highlight the application of the local flexibility market in three scenarios. The local market framework could postpone grid upgrades, reduce energy costs and increase distribution grids’ hosting capacity.

Electricity Price Forecasting Using Recurrent Neural Networks

Abstract: Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and cannot outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use multi-layer Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with three-year rolling window and compared the results with the RNNs. In our experiments, three-layered GRUs outperformed all other neural network structures and state-of-the-art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

Biological Pretreatment Strategies for Second-Generation Lignocellulosic Resources to Enhance Biogas Production

Abstract: With regard to social and environmental sustainability, second-generation biofuel and biogas production from lignocellulosic material provides considerable potential, since lignocellulose represents an inexhaustible, ubiquitous natural resource, and is therefore one important step towards independence from fossil fuel combustion. However, the highly heterogeneous structure and recalcitrant nature of lignocellulose restricts its commercial utilization in biogas plants. Improvements therefore rely on effective pretreatment methods to overcome structural impediments, thus facilitating the accessibility and digestibility of (ligno)cellulosic substrates during anaerobic digestion. While chemical and physical pretreatment strategies exhibit inherent drawbacks including the formation of inhibitory products, biological pretreatment is increasingly being advocated as an environmentally friendly process with low energy input, low disposal costs, and milder operating conditions. Nevertheless, the promising potential of biological pretreatment techniques is not yet fully exploited. Hence, we intended to provide a detailed insight into currently applied pretreatment techniques, with a special focus on biological ones for downstream processing of lignocellulosic biomass in anaerobic digestion.

Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control

Abstract: Field oriented control (FOC), direct torque control (DTC) and finite set model predictive control (FS-MPC) are different strategies for high performance electrical drive systems. FOC uses linear controllers and pulse width modulation (PWM) to control the fundamental components of the load voltages. On the other hand, DTC and FS-MPC are nonlinear strategies that generate directly the voltage vectors in the absence of a modulator. This paper presents all three methods starting from theoretic operating principles, control structures and implementation. Experimental assessment is performed to discuss their advantages and limitations in detail. As main conclusions of this work, it is affirmed that different strategies have their own merits and all meet the requirements of modern high performance drives.

Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Abstract: Responsible, efficient and environmentally aware energy consumption behavior is becoming a necessity for the reliable modern electricity grid. In this paper, we present an intelligent data mining model to analyze, forecast and visualize energy time series to uncover various temporal energy consumption patterns. These patterns define the appliance usage in terms of association with time such as hour of the day, period of the day, weekday, week, month and season of the year as well as appliance-appliance associations in a household, which are key factors to infer and analyze the impact of consumers’ energy consumption behavior and energy forecasting trend. This is challenging since it is not trivial to determine the multiple relationships among different appliances usage from concurrent streams of data. Also, it is difficult to derive accurate relationships between interval-based events where multiple appliance usages persist for some duration. To overcome these challenges, we propose unsupervised data clustering and frequent pattern mining analysis on energy time series, and Bayesian network prediction for energy usage forecasting. We perform extensive experiments using real-world context-rich smart meter datasets. The accuracy results of identifying appliance usage patterns using the proposed model outperformed Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) at each stage while attaining a combined accuracy of 81.82%, 85.90%, 89.58% for 25%, 50% and 75% of the training data size respectively. Moreover, we achieved energy consumption forecast accuracies of 81.89% for short-term (hourly) and 75.88%, 79.23%, 74.74%, and 72.81% for the long-term; i.e., day, week, month, and season respectively.

Peer to Peer Distributed Energy Trading in Smart Grids: A Survey

Abstract: Due to the expansion of distributed renewable energy resources, peer to peer energy trading (P2P DET) is expected to be one of the key elements of next generation power systems. P2P DET can provide various benefits such as creating a competitive energy market, reducing power outages, increasing overall efficiency of power systems and supplementing alternative sources of energy according to user preferences. Because of these promising advantages, P2P DET has attracted the attention of several researchers. Current research related to P2P DET include demand response optimization, power routing, network communication, security and privacy. This paper presents a review of the main research topics revolving around P2P DET. Particularly, we present a comprehensive survey of existing demand response optimization models, power routing devices and power routing algorithms. We also identify some key challenges faced in realizing P2P DET. Furthermore, we discuss state of the art enabling technologies such as Energy Internet, Blockchain and Software Defined Networking (SDN) and we provide insights into future research directions.

A Review of Particulate Number (PN) Emissions from Gasoline Direct Injection (GDI) Engines and Their Control Techniques

Abstract: Particulate Matter (PM) emissions from gasoline direct injection (GDI) engines, particularly Particle Number (PN) emissions, have been studied intensively in both academia and industry because of the adverse effects of ultrafine PM emissions on human health and other environmental concerns. GDI engines are known to emit a higher number of PN emissions (on an engine-out basis) than Port Fuel Injection (PFI) engines, due to the reduced mixture homogeneity in GDI engines. Euro 6 emission standards have been introduced in Europe (and similarly in China) to limit PN emissions from GDI engines. This article summarises the current state of research in GDI PN emissions (engine-out) including a discussion of PN formation, and the characteristics of PN emissions from GDI engines. The effect of key GDI engine operating parameters is analysed, including air-fuel ratio, ignition and injection timing, injection pressure, and EGR; in addition the effect of fuel composition on particulate emissions is explored, including the effect of oxygenate components such as ethanol.

Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting

Abstract: Photovoltaic power has great volatility and intermittency due to environmental factors. Forecasting photovoltaic power is of great significance to ensure the safe and economical operation of distribution network. This paper proposes a novel approach to forecast short-term photovoltaic power based on a gated recurrent unit (GRU) network. Firstly, the Pearson coefficient is used to extract the main features that affect photovoltaic power output at the next moment, and qualitatively analyze the relationship between the historical photovoltaic power and the future photovoltaic power output. Secondly, the K-means method is utilized to divide training sets into several groups based on the similarities of each feature, and then GRU network training is applied to each group. The output of each GRU network is averaged to obtain the photovoltaic power output at the next moment. The case study shows that the proposed approach can effectively consider the influence of features and historical photovoltaic power on the future photovoltaic power output, and has higher accuracy than the traditional methods.

Prosumer Communities and Relationships in Smart Grids: A Literature Review, Evolution and Future Directions

Abstract: Smart grids are robust, self-healing networks that allow bidirectional propagation of energy and information within the utility grid This introduces a new type of energy user who consumes, produces, stores and shares energy with other grid users Such a user is called a “prosumer” Prosumers’ participation in the smart grid is critical for the sustainability and long-term efficiency of the energy sharing process Thus, prosumer management has attracted increasing attention among researchers in recent years This paper systematically examines the literature on prosumer community based smart grid by reviewing relevant literature published from 2009 to 2018 in reputed energy and technology journals We specifically focus on two dimensions namely prosumer community groups and prosumer relationships Based on the evaluated literature, we present eight propositions and thoroughly describe several future research directions