Distribution Of Energy Demand Research Articles

Energy demand distribution and environmental impact assessment of chitosan production from shrimp shells

Step towards resilience and sustainability through exploring renewable biomass and waste streams to produce higher-added value products and energy is among key aspects for closing the loops, saving resources, and reducing the resource and emission footprints. In that respective, crustacean shells waste can offer rich spectre of valuable compounds such as proteins, chitin, carotenoids. This waste is produced in large quantities worldwide, thus allowing for commercial valorisation. An overview of technologies is undertaken for more sustainable and environmentally friendly chitosan production via chitin isolation and conversion and compared to the conventional processes. Furthermore, an assessment of the environmental burden and energy demand distribution for conventional and more sustainable alternative processes was performed, based on lab-scale experimental data. Three different chitin extraction routes and three distinct chitosan conversion processes were considered and compared for their greenhouse gas footprint, abiotic depletion, acidification, eutrophication and other potentials. Finally, the energy demand distribution was analysed considering electricity production patterns from three European countries, Slovenia, Portugal and Norway. The results showed that alternatives 3-A and 3-B (conventional eco-solvents - conventional deacetylation with 40 % and 50 % NaOH) generate the lowest environmental burden (184 g CO2 eq./g chitosan). Electricity was the main hotspot of the processes, used either for extraction, plasma treatment or deacetylation. The sensitivity analysis proved that the Norwegian electricity mix has the lowest environmental impact (4.2 g CO2 eq./g chitosan). This study highlights the impact of blue biorefineries by transforming marine waste to valuable biopolymers such as chitin and chitosan.

Correlation between the Production of Electricity by Offshore Wind Farms and the Demand for Electricity in Polish Conditions

Energy transition forcing a change in the structure of the electricity generation system is a particularly difficult task in countries such as Poland, where the dominant source of energy is fossil fuels. Due to the nature of renewable sources (stochastic and seasonally variable), it is necessary to study their impact on the power system. Much research was conducted on this subject. They consider modelling power systems in terms of dealing with an increasing amount of renewable energy sources, stabilization of electricity generation or environmental aspects. This article examines one of the key sources of future power systems—offshore wind turbines (OWT). The influence of offshore wind sources on the power system in the fields of stability of generation, methods of regulatory strategies, and economics were examined. One of the aspects that are less considered is the correlation of energy production in OWT with energy demand and with generation in other renewable energy sources, especially in the region of the southern Baltic Sea and the distribution of energy demand in countries such as Poland. The key aspect of the research is to fill this gap. The obtained results indicate that the average monthly power generation in OWT is strongly positively correlated with the demand, and the hourly average is positively correlated moderately. Correlation between generation in OWT and photovoltaic sources is very high negative, and between onshore and offshore wind turbines is highly positive. The study indicates that the OWT has a significant potential for the development and replacement of conventional sources, due to the very high capacity and a positive correlation with demand. Moreover, future offshore wind farms can cooperate with photovoltaic sources as these sources complement each other. On the other hand, a significant saturation of the system with offshore and onshore wind sources may pose a threat to the power system due to their positive correlation.

An Estimate on Distribution of Hysteretic Energy Demand in Seismic Precast Concrete Frame Structures

ABSTRACT New procedures are proposed for estimating the hysteretic energy demand distribution of precast concrete frames using the shear-bending story model and energy spectra. An efficient beam element is specifically developed to account for the effects of some precast connection parameters. The rationality of the proposed procedures is systematically demonstrated. The distribution of hysteretic energy demand is found to be closely related to the type, location and stiffness of connections. It is feasible to utilize the relationships between the hysteretic energy demand distribution and connection parameters to achieve an optimal energy-based seismic design for minimizing structural damage.

Analysis of annual energy consumption by a warehouse building

This analysis was carried out to present the distribution of energy demand for air heating, air cooling and technology equipment for a warehouse through the year. The supplied energy is used to maintain the assumed temperatures in the rooms and for the needs of technology. During the work, the calculation model of the building was prepared and imported into the calculation program. The simulation was based on the planned building parameters (partitions structure, dimensions, technology) taken from the architectural executive design. Several versions of the structure of the construction and technical equipment have been analyzed. The obtained results show differences in the formation of energy demand and indoor room conditions.

Conditional density forecast of China’s energy demand via QRNN model

ABSTRACTWe propose a new method for conditional density forecast of China’s energy demand through quantile regression neural network (QRNN). This method has at least two advantages. First, it is flexible to explore the true nonlinearity in the energy demand system via neural network structure. Second, it is able to describe the whole conditional distribution of energy demand via quantile regression. In the empirical study on China’s energy demand, QRNN outperforms several classical methods in terms of forecast accuracy both in-sample and out-of-sample. Considering China’s economic and social environment, we set a scenario for predictors and forecast the conditional density of China’s energy demand from 2015 to 2020. The empirical results show that the conditional density curve moves to right and its dispersion increases over time, which indicates that the energy demand in China will keep growing with an average annual rate of 9.672% and its uncertainty is enlarged with 42.210%.

Modeling of Energy Demand of a High-Tech Greenhouse in Warm Climate Based on Bayesian Networks

This work analyzes energy demand in a High-Tech greenhouse and its characterization, with the objective of building and evaluating classification models based on Bayesian networks. The utility of these models resides in their capacity of perceiving relations among variables in the greenhouse by identifying probabilistic dependences between them and their ability to make predictions without the need of observing all the variables present in the model. In this way they provide a useful tool for an energetic control system design. In this paper the acquisition data system used in order to collect the dataset studied is described. The energy demand distribution is analyzed and different discretization techniques are applied to reduce its dimensionality, paying particular attention to their impact on the classification model’s performance. A comparison between the different classification models applied is performed.

Design of District Heating System in Rural Area Considering Biomass Resource and Energy Demand Distribution

Design of District Heating System in Rural Area Considering Biomass Resource and Energy Demand Distribution

Degree-days, population distribution and heating energy consumption: The Italian case

SYNOPSIS Degree-days are widely utilised to evaluate heating energy consumption in buildings. An auxiliary criterion, which can provide useful information about the diffusion of energy demand in a given territory, is the analysis of the population distribution performed on the basis of the local winter climate. This paper studies the possibility of correlating heating energy consumption in buildings and the territorial distribution of degree-days and population. The distribution of the Italian population was analysed in relation with heating degree-days and population-weighted degree-days were calculated. Correlated consumption indexes were then introduced and their nationwide distribution was analysed. These indexes enabled us to locate the distribution of energy demand and pick out the areas where consumption density is highest. The predictive method was successfully validated by comparison with real data on natural gas consumption in Italy for the winter heating of buildings.