scholarly journals Day-ahead forecasting of grid carbon intensity in support of heating, ventilation and air-conditioning plant demand response decision-making to reduce carbon emissions

2018 ◽  
Vol 39 (6) ◽  
pp. 749-760 ◽  
Author(s):  
Gordon Lowry
Keyword(s):  
Demand Response ◽  
Carbon Emissions ◽  
Air Conditioning ◽  
Network Models ◽  
Time Of Day ◽  
Electrical Heating ◽  
Carbon Intensity ◽  
High Carbon ◽  
Neural Network Models ◽  
Electricity Grid

Electrical heating, ventilation and air-conditioning loads in buildings are suitable candidates for use in demand response activity. This paper demonstrates a method to support planned demand response actions intended explicitly to reduce carbon emissions. Demand response is conventionally adopted to aid the operation of electricity grids and can lead to greater efficiency; here it is planned to target times of day when electricity is generated with high carbon intensity. Operators of heating, ventilation and air-conditioning plant and occupants of conditioned spaces can plan when to arrange shutdown of plant once they can foresee the opportune time of day for carbon saving. It is shown that the carbon intensity of the mainland UK electricity grid varies markedly throughout the day, but that this tends to follow daily and weekly seasonal patterns. To enable planning of demand response, 24 h ahead forecast models of grid carbon intensity are developed that are not dependent on collecting multiple exogenous data sets. In forecasting half-hour periods of high carbon intensity either linear autoregressive or non-linear artificial neural network models can be used, but a daily seasonal autoregressive model is shown to provide a 20% improvement in carbon reduction. Practical application: The forecast method demonstrated in the paper would enable building operators to plan demand response activity to target times of high carbon intensity on the UK electricity grid. The method would be easy to implement as the only data required are publicly available.

scholarly journals Towards net-zero carbon performance: using demand side management and a low carbon grid to reduce operational carbon emissions in a UK public office

2021 ◽  
Vol 2069 (1) ◽  
pp. 012150
Author(s):  
E Burman ◽  
N Jain ◽  
M de-Borja-Torrejón
Keyword(s):  
Carbon Emissions ◽  
Demand Side Management ◽  
Carbon Intensity ◽  
Grid Integration ◽  
Demand Side ◽  
Low Carbon ◽  
Electricity Grid ◽  
Integration Strategy ◽  
Carbon Performance ◽  
And Control

Abstract This paper investigates the performance of an office building that has achieved a low carbon performance in practice thanks to a performance contract and Soft Landings approach. The findings show the potential of this building for further de-carbonisation as a result of electrification of heating and load shifting to take advantage of a low carbon electricity grid. Whilst retrospective modelling based on the past carbon intensity data shows the effectiveness of demand-side management, assessment of the existing smart readiness of the building revealed that the building services and control strategy are not fully equipped with the data analytics and carbon or price signal responsiveness required to facilitate grid integration. The environmental strategy and procurement method used for this building combined with an effective grid integration strategy can serve as a prototype for low carbon design to achieve the ever stringent carbon emissions objectives set out for the non-domestic buildings.

scholarly journals Greenhouse Gas Reductions under Low Carbon Fuel Standards?

2009 ◽  
Vol 1 (1) ◽  
pp. 106-146 ◽  
Author(s):  
Stephen P Holland ◽  
Jonathan E Hughes ◽  
Christopher R Knittel
Keyword(s):  
Greenhouse Gas ◽  
Carbon Emissions ◽  
Cost Effective ◽  
Carbon Intensity ◽  
Energy Prices ◽  
Low Carbon ◽  
High Carbon ◽  
Fuel Production ◽  
Abatement Costs ◽  
Effective Policy

A low carbon fuel standard (LCFS) seeks to reduce greenhouse gas emissions by limiting the carbon intensity of fuels. We show this decreases high carbon fuel production but increases low carbon fuel production, possibly increasing net carbon emissions. The LCFS cannot be efficient, and the best LCFS may be nonbinding. We simulate a national LCFS on gasoline and ethanol. For a broad parameter range, emissions decrease, energy prices increase, abatement costs are large ($80–$760 billion annually), and average abatement costs are large ($307–$2,272 per CO2 metric ton). A cost effective policy has much lower average abatement costs ($60–$868). (JEL Q54, Q58)

scholarly journals Development and Utilization of Renewable Energy Based on Carbon Emission Reduction—Evaluation of Multiple MCDM Methods

2021 ◽  
Vol 13 (17) ◽  
pp. 9822
Author(s):  
Tao Li ◽  
Ang Li ◽  
Yimiao Song
Keyword(s):  
Renewable Energy ◽  
Wind Power ◽  
Carbon Emissions ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Carbon Intensity ◽  
High Carbon ◽  
Carbon Emission Reduction ◽  
Photovoltaic Power ◽  
Development And Utilization

With the proposed target of carbon peak and carbon neutralization, the development and utilization of renewable energy with the goal of carbon emission reduction is becoming increasingly important in China. We used the analytic hierarchy process (ANP) and a variety of MCDM methods to quantitatively evaluate renewable energy indicators. This study measured the sequence and differences of the development and utilization of renewable energy in different regions from the point of view of carbon emission reduction, which provides a new analytical perspective for the utilization and distribution of renewable energy in China and a solution based on renewable energy for achieving the goal of carbon emission reduction as soon as possible. The reliability of the evaluation system was further enhanced by confirmation through a variety of methods. The results show that the environment and carbon dimensions are the primary criteria to evaluate the priority of renewable energy under carbon emission reduction. In the overall choice of renewable energy, photovoltaic energy is the best solution. After dividing regions according to carbon emission intensity and resource endowment, areas with serious carbon emissions are suitable for the development of hydropower; areas with sub-serious carbon emissions should give priority to the development of photovoltaic or wind power; high-carbon intensity area I should vigorously develop wind power; high-carbon intensity area II should focus on developing photovoltaic power; second high-carbon intensity areas I and II are suitable for the development of wind power and photovoltaic power; and second high-carbon intensity areas III and IV are the most suitable for hydropower.

scholarly journals Predicting Crime and Other Uses of Neural Networks in Police Decision Making

2021 ◽  
Vol 12 ◽  
Author(s):  
Steven Walczak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Law Enforcement ◽  
Prediction Models ◽  
Network Models ◽  
Time Of Day ◽  
Neural Network Models ◽  
Police Decision Making ◽  
The Neural Network

Neural networks are a machine learning method that excel in solving classification and forecasting problems. They have also been shown to be a useful tool for working with big data oriented environments such as law enforcement. This article reviews and examines existing research on the utilization of neural networks for forecasting crime and other police decision making problem solving. Neural network models to predict specific types of crime using location and time information and to predict a crime’s location when given the crime and time of day are developed to demonstrate the application of neural networks to police decision making. The neural network crime prediction models utilize geo-spatiality to provide immediate information on crimes to enhance law enforcement decision making. The neural network models are able to predict the type of crime being committed 16.4% of the time for 27 different types of crime or 27.1% of the time when similar crimes are grouped into seven categories of crime. The location prediction neural networks are able to predict the zip code location or adjacent location 31.2% of the time.

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Measuring the World

2018 ◽  
Author(s):  
Ann-Sophie Barwich
Keyword(s):  
Sensory Information ◽  
Network Models ◽  
Model System ◽  
Stimulus Input ◽  
Expectancy Effects ◽  
Neural Network Models ◽  
Perceptual Object ◽  
External Objects ◽  
The Common ◽  
The Brain

How much does stimulus input shape perception? The common-sense view is that our perceptions are representations of objects and their features and that the stimulus structures the perceptual object. The problem for this view concerns perceptual biases as responsible for distortions and the subjectivity of perceptual experience. These biases are increasingly studied as constitutive factors of brain processes in recent neuroscience. In neural network models the brain is said to cope with the plethora of sensory information by predicting stimulus regularities on the basis of previous experiences. Drawing on this development, this chapter analyses perceptions as processes. Looking at olfaction as a model system, it argues for the need to abandon a stimulus-centred perspective, where smells are thought of as stable percepts, computationally linked to external objects such as odorous molecules. Perception here is presented as a measure of changing signal ratios in an environment informed by expectancy effects from top-down processes.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

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