Listing 1 - 10 of 14 | << page >> |
Sort by
|
Choose an application
Newpharma is a Belgian online pharmacy operating on several markets such as Belgium, France, Holland and Germany. They sell pharmaceutical and para-pharmaceutical products on the Internet and deliver the parcel to the patients. They position themselves as Belgian leader and compete on the other international markets to gain market share and rank the top-3. To achieve their objectives, they developed a large offer for customers and want to propose the best prices. Looking at the objectives of the company we oriented the pricing strategy to an everyday low pricing strategy. This implies a heavy control of the costs structure. In order to be able to low-price a cost domination strategy is necessary to succeed. You will observe that three main components basically influence the pricing strategy and the price setting: the customer, the costs and the competition.
Choose an application
The practice of setting a single price that all buyers pay is slowly becoming a thing of the past. Today's marketplace requires firms to develop innovative pricing strategies to remain competitive. Is it better to bundle goods or price them separately? What type of online auction will generate the most revenue? The purpose of this book is to use microeconomic theory to determine which pricing strategies will succeed, and under what conditions.
Pricing. --- Open price system. --- Internet auctions. --- Price discrimination --- bundling --- price skimming --- price penetration --- online auctions --- English auction --- Dutch auction --- first-price sealed bid auction --- second-price sealed bid auction --- price elasticity --- consumer surplus --- two-part tariffs --- quantity discounts --- quality choices --- tying --- peak-load pricing --- dynamic pricing --- e-commerce --- pricing --- Robinson-Patman Act --- winner's curse --- reference price --- private value auction --- common value auction
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
History of engineering & technology --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
History of engineering & technology --- autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The purpose of this Special Issue is to create an an academic platform whereby high-quality research papers are published on the applications of innovative AI algorithms to transportation planning and operation. The authors present their original research articles related to the applications of AI or machine-learning techniques to transportation planning and operation. The topics of the articles encompass traffic surveillance, traffic safety, vehicle emission reduction, congestion management, traffic speed forecasting, and ride sharing strategy.
autoencoder --- deep learning --- traffic volume --- vehicle counting --- CycleGAN --- bottleneck and gridlock identification --- gridlock prediction --- urban road network --- long short-term memory --- link embedding --- traffic speed prediction --- traffic flow centrality --- reachability analysis --- spatio-temporal data --- artificial neural network --- context-awareness --- dynamic pricing --- reinforcement learning --- ridesharing --- supply improvement --- taxi --- preventive automated driving system --- automated vehicle --- traffic accidents --- deep neural networks --- vehicle GPS data --- driving cycle --- micro-level vehicle emission estimation --- link emission factors --- MOVES --- black ice --- CNN --- prevention
Choose an application
The practice of setting a single price that all buyers pay is slowly becoming a thing of the past. Today's marketplace requires firms to develop innovative pricing strategies to remain competitive. Is it better to bundle goods or price them separately? What type of online auction will generate the most revenue? The purpose of this book is to use microeconomic theory to determine which pricing strategies will succeed, and under what conditions.
Pricing. --- Open price system. --- Internet auctions. --- Bundling --- common value auction --- consumer surplus --- Dutch auction --- dynamic pricing --- e-commerce --- English auction --- first-price sealed bid auction --- online auctions --- peak-load pricing --- price discrimination --- price elasticity --- price penetration --- price skimming --- pricing --- private value auction --- quality choices --- quantity discounts --- reference price --- Robinson- Patman Act --- second-price sealed bid auction --- surge pricing --- two-part tariffs --- tying --- winner's curse
Choose an application
This book covers applied research on smart energy systems, smart grids, smart energy homes, smart energy products and services, and the advanced applications thereof, in the context of demand response and grid interactions. In particular, this book is focused on interdisciplinary research results that combine technical, social, environmental, and economic aspects of smart energy systems and smart energy products. Moreover, several chapters are based on the evaluation of real life cases, energy pilots, prototypes of smart energy products, and end user surveys and interviews.
load duration curve --- n/a --- ancillary services --- electricity pricing --- renewable energy --- consumption patterns --- smart home technology --- self-consumption --- solar charging --- smart appliances --- spatial and temporal aggregation --- energy products and services --- solar mobility --- dynamic pricing --- frequency containment reserve --- heat pumps --- users --- FCR --- renewable energy transition --- privacy protection --- end-users --- self-sufficiency --- energy system analysis --- reliability --- photovoltaic systems --- stakeholders --- uncertainty --- smart grids --- flexibility --- prosumer --- demand shifting --- resonance instability --- energy management --- grid-connected inverter --- electricity market --- demand management --- smart metering --- photovoltaic --- aggregator --- smart product design --- internal attack --- pseudo-random function --- power quality --- power systems simulation --- demand response
Choose an application
Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid. These systems may include photovoltaic generators, wind turbines, hydro turbines or any other renewable electrical generator. Usually, this kind of system includes electricity storage (commonly lead-acid batteries, but also other types of storage can be used). In some cases, a backup generator (usually powered by fossil fuel, diesel or gasoline) is part of the hybrid system. The modelling of the components, the control of the system and the simulation of the performance of the whole system are necessary to evaluate the system technically and economically. The optimization of the sizing and/or the control is also an important task in this kind of system.
Research & information: general --- renewable energy --- low-temperature energy storage --- SOC --- simulation --- sustainability --- greenhouse gas emission --- economic feasibility --- photovoltaic systems --- MPPT --- partial shading condition --- transfer reinforcement learning --- space decomposition --- microgrids --- energy management --- optimization --- photovoltaic --- energy storage --- demand response program (DRP) --- photovoltaic system (PV) --- pumped heat energy storage (PHES) --- critical peak pricing (CPP) DRP --- time-ahead dynamic pricing (TADP) DRP --- loss of power supply probability (LPSP) --- energy storage system (ESS) --- Multi-Objective Particle Swarm Optimization (MOPSO) --- pitch control --- permanent magnet-synchronous generator (PMSG) --- limit extracted power --- nonlinear adaptive control (NAC) --- perturbation observer --- vanadium redox flow battery --- genetic algorithm --- binary particle swarm optimization --- time-varying mirrored S-shaped transfer function --- greenhouse gas emissions --- heliostat --- sun tracking --- solar energy --- embedded system --- fuzzy logic control --- center of sums defuzzification method --- renewable energy --- low-temperature energy storage --- SOC --- simulation --- sustainability --- greenhouse gas emission --- economic feasibility --- photovoltaic systems --- MPPT --- partial shading condition --- transfer reinforcement learning --- space decomposition --- microgrids --- energy management --- optimization --- photovoltaic --- energy storage --- demand response program (DRP) --- photovoltaic system (PV) --- pumped heat energy storage (PHES) --- critical peak pricing (CPP) DRP --- time-ahead dynamic pricing (TADP) DRP --- loss of power supply probability (LPSP) --- energy storage system (ESS) --- Multi-Objective Particle Swarm Optimization (MOPSO) --- pitch control --- permanent magnet-synchronous generator (PMSG) --- limit extracted power --- nonlinear adaptive control (NAC) --- perturbation observer --- vanadium redox flow battery --- genetic algorithm --- binary particle swarm optimization --- time-varying mirrored S-shaped transfer function --- greenhouse gas emissions --- heliostat --- sun tracking --- solar energy --- embedded system --- fuzzy logic control --- center of sums defuzzification method
Choose an application
The electric power sector is poised for transformative changes. Improvements in the cost and performance of a range of distributed energy generation (DG) technologies and the potential for breakthroughs in distributed energy storage (DS) are creating new options for onsite power generation and storage, driving increasing adoption and impacting utility distribution system operations. In addition, changing uses and use patterns for electricity—from plug-in electric vehicles (EVs) to demand response (DR)—are altering demands placed on the electric power system. Finally, the infusion of new information and communications technology (ICT) into the electric system and its markets is enabling the collection of immense volumes of data on power sector operations and use; unprecedented control of generation, networks, and loads; and new opportunities for the delivery of energy services. In this Special Issue of Energies, research papers on topics related to the integration of distributed energy resources (DG, DS, EV, and DR) are included. From technologies to software tools to system-wide evaluations, the impacts of all aforementioned distributed resources on both operation and planning are examined.
History of engineering & technology --- machine learning --- microgrids --- optimisation methods --- power systems --- reinforcement learning --- high penetration --- renewable energy --- adaptability planning --- source-grid coordination --- renewable electricity distribution for public space --- sustainability assessment model --- integrated assessment for public space --- tripartite altruism --- urban renewable energy --- ecological infrastructures --- Micro-grids --- continuity of supply --- power distribution --- power system planning --- decentralized control --- small hydropower plants --- microgrid --- emergency control --- recloser --- synchronous coupler --- power systems stability --- power system operation --- power system security --- renewable energy integration --- load flow analysis --- congestion management --- distributed generation curtailment --- demand side management --- demand response --- cyber-physical systems --- dynamic pricing --- load forecasting --- attack detection --- photovoltaics --- distributed energy resources (DERs) --- grid impact --- power quality --- low-voltage distribution network --- inverter regulation --- electric vehicles --- uncontrolled charging --- delayed charging --- controlled charging --- V2G --- V2B --- V2H --- peak shaving --- valley filling --- renewable energy sources
Choose an application
Standalone (off-grid) renewable energy systems supply electricity in places where there is no access to a standard electrical grid. These systems may include photovoltaic generators, wind turbines, hydro turbines or any other renewable electrical generator. Usually, this kind of system includes electricity storage (commonly lead-acid batteries, but also other types of storage can be used). In some cases, a backup generator (usually powered by fossil fuel, diesel or gasoline) is part of the hybrid system. The modelling of the components, the control of the system and the simulation of the performance of the whole system are necessary to evaluate the system technically and economically. The optimization of the sizing and/or the control is also an important task in this kind of system.
Research & information: general --- renewable energy --- low-temperature energy storage --- SOC --- simulation --- sustainability --- greenhouse gas emission --- economic feasibility --- photovoltaic systems --- MPPT --- partial shading condition --- transfer reinforcement learning --- space decomposition --- microgrids --- energy management --- optimization --- photovoltaic --- energy storage --- demand response program (DRP) --- photovoltaic system (PV) --- pumped heat energy storage (PHES) --- critical peak pricing (CPP) DRP --- time-ahead dynamic pricing (TADP) DRP --- loss of power supply probability (LPSP) --- energy storage system (ESS) --- Multi-Objective Particle Swarm Optimization (MOPSO) --- pitch control --- permanent magnet-synchronous generator (PMSG) --- limit extracted power --- nonlinear adaptive control (NAC) --- perturbation observer --- vanadium redox flow battery --- genetic algorithm --- binary particle swarm optimization --- time-varying mirrored S-shaped transfer function --- greenhouse gas emissions --- heliostat --- sun tracking --- solar energy --- embedded system --- fuzzy logic control --- center of sums defuzzification method --- n/a
Listing 1 - 10 of 14 | << page >> |
Sort by
|