Analysis Of Electricity Consumption Research Articles

Evaluation Method for Energy Saving Effect of Reflective Thermal Insulation Coatings

According to the climate and energy saving characteristics in the hot summer and cold winter zone, especially in view of requirements for thermal insulation in summer, reflective thermal insulation coatings (RTIc) are gradually applied in building energy efficiency of exterior envelope. A feasible evaluation method for energy saving effect is very important for acceptance of energy efficient building construction. In this paper, thermal resistance has been used as the evaluation index. The energy efficiency of the reflective thermal insulation coatings was investigated. Four hot boxes were applied contrast test. Based on the qualitative and quantitative analysis of electricity consumption within 36 hours and comparative analysis of heating process simulating sunlight environment, thermal resistance of RTIc can be calculated on the basis of the correlation between power saving rate and thermal resistance so that energy saving effect of RTIc can be quantitatively characterized.

Development of methodology for the assessment of changes in household electricity consumption and calculation of CO<SUB align="right">2 emissions

This study outlines a methodology for assessing changes in household electricity consumption and CO2 emissions. The method is proposed to analyse large datasets of residential electricity consumption in the case study of the first smart metering pilot project in Latvia. The goal of the project is to achieve a reduction of electricity consumption in households by 10%. In order to do so project aims to increase household user's awareness of smart technologies, as well as to promote households' involvement in energy efficiency measures. The proposed methodology is based on several steps, including: 1) baseline situation analysis; 2) normalisation of electricity consumption data; 3) identification of the factors affecting household electricity consumption; 4) an empirical analysis of households' electricity consumption using regression analysis; 5) assessment of changes in electricity consumption at the end of evaluation period and 6) calculation of CO2 emissions. Finally, the first results of the smart metering pilot project in nine-month period have been presented. The recommendations for policy development on promoting smart metering have been raised at the end of this paper.

ANALYSIS OF ENERGY CONSUMPTION IN THE FACILITIES OF THE CIECHANÓW DAIRY COOPERATIVE

The work presents the analysis of electric energy consumption in individual buildings of the Ciechanow Dairy Cooperative in 2011. The plant includes a cool storage facility, a boiler house, a compression room and CIP (Cleaning In Place) stations. The plant manufactures liquid milk, cream, cheese, cottage cheese, cream cheese, butter and yoghurts. In 2011, the total electric energy consumption in the Cechanow Dairy Cooperative was 873 114.0 kWh. The highest energy consumption (556 430.0 kWh0 was recorded for the cooler (63.7% of total consumption) and the lowest for CIP stations (33 084.0 kWh). The average electric energy consumption in the Cooperative plant was 72 759.5 kWh, the highest (104 800.6 kWh) in November and the lowest (40 000.6 kWh) in March.

탈석유화와 전기화의 관계 분석

, , Abstract - After the oil crisis in 1970s, many countries have tried to reduce oil dependency. Especially, in Korea, rapid declining oil consumption has speedily brought to electrification and a surge in electricity demand. This paper attempts to estimate the relationship between declining oil use and electrification in Korea using OECD panel data covering from 1985 to 2011. To this end, random effect model and fixed effect model are employed. The increase in the ratio of energy oil to total energy consumption by 10%p leads to reduce the electricity demand by about 15%. This result can be useful information to cope with the recent crisis of electric power. In addition, industrial sector is ranked in forth the ratio of industrial electricity use to total electricity use according to the result of comparative analysis of electricity consumption by use in OECD countries. Therefore, industrial sector should be treated as the main target of demand-side management policies for electricity.

Calculation method for electricity end-use for residential lighting

Knowledge of the electricity demand for different electrical appliances in households is very important in the work to reduce electricity use in households. Metering of end-uses is expensive and time consuming and therefore other methods for calculation of end-use electricity can be very useful. This paper presents a method to calculate the electricity used for lighting in households based on regression analysis of daily electricity consumption, out-door temperatures and the length of daylight at the same time and location. The method is illustrated with analyses of 45 Norwegian households. The electricity use for lighting in an average Norwegian household is calculated to 1050 kWh/year or 6% of total electricity use. The results are comparable to metering results of lighting in other studies in the Nordic countries. The methodology can also be used to compensate for the seasonal effect when metering electricity for lighting less than a year. When smart meters are more commonly available, the possible adaption of this method will increase, and the need for end-use demand calculations will still be present.

Building Fan Coil Electric Consumption Analysis with Fuzzy Approaches for Fault Detection and Diagnosis

In the building energy efficiency field, developing automatic and accurate fault detection and diagnosis methods is necessary in order to ensure optimal operations of systems and to save energy. In this paper first, fault detection analysis based on statistical methods where anomalies are detected through a comparison with neighborhood and averaged fault-free values and through a clustering technique is performed. Following the fault detection step, a fault diagnosis analysis based on fuzzy sets and fuzzy logic is implemented. Experimentation is carried out over a one day monitoring data set in December 2013 for the fan coil electric consumption of an actual office building located at ENEA ‘Casaccia’ Research Centre. Results show the effectiveness of proposed approaches in automatic detection and diagnosis of abnormal building fan coil electric consumption.

A comprehensive analysis of electric energy consumption of single point incremental forming processes

Abstract Production processes, as used for discrete part manufacturing, are responsible for a substantial part of the environmental impact of products, but are still poorly documented in terms of environmental impact. The present paper proposes a comprehensive energetic analysis of Single Point Incremental Forming Processes (SPIF). The three most commonly used machine tool architectures able to perform SPIF operations have been taken into account: a CNC milling machine, a six-axes robot as well as the dedicated AMINO machine tool were analyzed from an electrical energy consumption point of view. For all the setups, a working cycle time study and power study were performed. Moreover the contribution of each sub-unit towards the total energy demand has been determined. The influence of the most relevant process parameters (e.g. feed rate, step down), has been determined. The three setups have been analyzed in order to identify the solutions with the highest energy efficiency for SPIF processes and a set of potential environmental friendly process control strategies are proposed. Finally, a parametric model, able to predict the energy consumption at the varying of the process parameters, is proposed.

Analysis of irregular electricity consumption

The paper analyzes the current electricity consumption modes. The problem of irregular electric power consumption has been noted. The advantages and disadvantages of systems of measuring electricity consumption of sinusoidal systems have been given and their inaccuracy under variable loading and non-sinusoidal operation modes has been proven. The influence of irregular energy consumption has been analyzed. It has been confirmed that the traditional power coefficient takes no account of uneven consumption. Power losses have been assessed, that appear in electrical network when its transfer to consumers, using the least possible value of power losses. The formula has been developed for calculating the power coefficient, which takes into account all inactive components of total power and describes any electricity consumption mode, while the cosφ - only in a particular case of symmetric sinusoidal load currents. The average value of power factor per time interval has been determined, which takes into account negative effects of irregular consumption, and depends only on a consumer. This coefficient also includes irregular consumption schedule. The corresponding conclusions were made.

Electricity Consumption Analysis Using Demographic Variables Case Study – Nakhonratchasima, Thailand

Electricity energy is vital in social and economic for nation development. The electricity consumption analysis plays an important role for sustainable energy and electricity resources management in the future. In this paper, the influence of demographical variables on the annual electricity consumption in Nakhonratchasima has been investigated by multiple regression analysis. It is founded that the electricity consumption correlated with four demographic variables, which are the number of electricity consumers, the amount of high speed diesel usages, the number of industrial factory and the number of employed labor force. The historical electricity consumption and all variables for the period 20022010 have been analyzed in 8 models for electricity prediction in 2011. In conclusion, the effective model has been selected by comparison of adjusted R2, mean absolute error (MAE) and root mean squared error (RMSE) of the proposed models. Model 8 is acceptable in relation to electricity consumption analysis with adjusted-R2, RMSE and MAE equal to 0.9980, 0.7540% and 0.6095% respectively. The results indicate that the model using all four variables has strong ability to predict future annual electricity consumption with 4,195,837,877 kWh in 2011.

An Exploratory Analysis of Electricity Consumption and Stock Prices: Evidence From OECD Countries

This study explores the impact of electricity consumption, as a proxy for economic activity, on stock prices for a panel of 23 Organization for Economic Cooperation and Development countries for the period 1992 to 2010. Heterogeneous panel cointegration tests reveal a long-run equilibrium relationship between stock prices and electricity consumption, while controlling for factors related to arbitrage pricing models and macroeconomic fundamentals. Finally, the panel error correction model shows bidirectional causality in both the short-run and long-run between electricity consumption and stock prices.

Analysis of Illumination and Electric Power Consumption of LED Lamp Installed in Ship's Cabin

Analysis of Illumination and Electric Power Consumption of LED Lamp Installed in Ship's Cabin

On the Links between Sustainable Wellbeing and Electric Energy Consumption

Access to electric energy is one of the drivers of wellbeing and universal access to energy has recently been identified as an important goal in the international community. A data-driven analysis of electric energy consumption and subjective wellbeing indicators is introduced to promote a broad conversation about relationships between energy poverty, global equity, subjective wellbeing measurements and good quality of life. The proposed approach directly focuses on the overall goal of attaining good and long lasting lives, the implications for future generations, the importance of ecosystems in enabling sustainable wellbeing and the needed capabilities in communities to flourish. Eventually, the proposed pathway should lead to a capabilities approach assessment for appropriate technologies. The results point out that although there is an important relationship between electric energy consumption and human wellbeing, a deeper look at electric energy access, subjective wellbeing indicators, local capabilities, and participatory decision making processes is needed to better prepare and implement long-term energy plans at local and global levels.

Electricity Consumption Analysis Using Spline Regression Models: The Case of a Turkish Province

Electricity Consumption Analysis Using Spline Regression Models: The Case of a Turkish Province

Electricity Consumption Analysis Using Spline Regression Models: The Case of a Turkish Province

Energy is one of the indispensible elements of human life and electrical energy is adopted as the most frequently used energy type. As this type of energy can not be stored at the present time, it has to be instantly consumed. In other words, the demand of the consumers has to be compensated, immediately. This paper employs to model the electrical consumption of Erzurum province in 2011 by spline regression and to decide whether a statistically seasonal variation exists for this consumption. The one-year data set of the investigation was obtained from Turkish Electricity Transmission Company Provincial Directorate of Erzurum and was analyzed by the agency of continuous partial polynomial spline regressions. This analysis determined three knots and fits linear, quadratic and cubic spline regression models.

Decoupling analysis of electricity consumption from economic growth in China

The purpose of this paper is to apply the decoupling index combined with the LMDI method to analyze the contribution of the factors which influence electricity consumption in China over the period 1991-2009. The main results are as follows: (1) Electricity consumption has been rising year by year with economic growth. Electricity consumption came to 427.37 Mtce in 2009, which accounted for 19.56% of total energy consumption. (2) China’s electricity intensity has continuously decreased during the 1990s and early 2000s, the decreasing trend has reserved since 2003. (3) The economic activity effect is the most important contributor to increase electricity consumption in China, the sector electricity share effect is another important factor leading to the rapid growth of electricity and the energy intensity effect plays the dominant role in decreasing electricity consumption. (4) The period from 1999 to 2007 represents a re-coupling effect, while the other time interval shows weak decoupling effect.

Electric Energy Consumption Analysis of SPIF Processes

Manufacturing processes, as used for discrete part manufacturing, are responsible for a substantial part of the environmental impact of products, but are still poorly documented in terms of environmental footprint. A thorough analysis on the causes affecting the environmental impact in metal forming processes, especially the innovative but very energy intensive sheet metal forming technologies required to form light-weight products, is nowadays necessary. Therefore, this paper presents an energy consumption analysis, including a power and time study, of Single Point Incremental Forming (SPIF) processes. First, the influence of the most relevant process parameters (e.g. feed rate, step down) as well as the material forming itself are analysed regarding the power demand. Moreover, a comparative study and related energy efficiency assay are carried out on two different machine tools. As the forming time proves to be the dominant factor for the total energy consumption, from environmental point of view, the overall results show many similarities with conventional machining processes. Finally, this paper reports on some potential improvement measures to reduce the SPIF energy consumption.

Feasibility analysis of medium-sized hotel's electrical energy consumption with hybrid systems

Abstract In this study, the case of meeting a hotel's electrical energy demand with hybrid systems has been examined by using four different scenarios. The HOMER program is used for different analyses. In the first case, when the renewable energy resources are insufficient, electrical energy is purchased from grid and in the cases, in which the production is much more than the need, electricity is sold to the grid. In three other cases, the electric demand of the hotel is met by using renewable energy and the amount, which is more than the need, is sold to the grid. As a result, at the present circumstances two scenarios were determined feasible. When the grid is used only selling surplus electricity and the electricity need of the hotel are met by only renewables, battery groups and converters are needed. So, this causes an increase in investment costs. Therefore, these situations must be supported with adequate incentives, to make these systems become more eligible.

An Adaptive-Network-Based Fuzzy Inference System-Data Envelopment Analysis Algorithm for Optimization of Long-Term Electricity Consumption, Forecasting and Policy Analysis: The Case of Seven Industrialized Countries

This article presents an adaptive-network-based fuzzy inference system (ANFIS)-data envelopment analysis (DEA) algorithm for improvement of long-term electricity consumption forecasting and analysis. Six models are proposed to forecast annual electricity demand. Six different membership functions and several linguistic variables are considered in building ANFIS. The proposed models consist of two input variables, namely, gross domestic product and population. All trained ANFIS are then compared with respect to mean absolute percentage error. To meet the best performance of the intelligent-based approaches, data are pre-processed (scaled) and finally our outputs are post-processed (returned to its original scale). DEA is used to optimize the electricity consumption as well as examine the behavior of electricity consumption. To show the applicability and superiority of the ANFIS-DEA algorithm, actual electricity consumption in the USA, Canada, Germany, United Kingdom (UK), Japan, France and Italy from 1980–2007 is considered. Electricity consumption is then forecasted up to 2015. The unique features of the ANFIS-DEA algorithm are: behavioral analysis and optimization in complex, non-linear and uncertain environments.

Measurements of electric energy consumption in a low energy building in Slovak climatic conditions

Buildings with heating demands <20 kWh/m2 usually use electric energy for covering their energy needs. The aim of the paper is an analysis of electric energy consumption in this type of building. Data were obtained by in-situ measurements. Electricity consumption for hot water preparation, ventilation and household appliances are presented. Also a comparison of the real consumption with the design, and the impact of a user to the total consumption are analysed. The results show that 60% of electricity consumption is during months December, January and February. The obtained results show, that another decreasing in electric energy consumption is possible.

Analysis and Methodology to Segregate Residential Electricity Consumption in Different Taxonomies

This work presents a two-stage model for the data analysis of electricity consumption. The first stage divides the consumption in two parts: weather- and illumination-related, and residual consumption, where weather-related consumption refers to heating, ventilation, and air conditioning (HVAC). Given the hourly total consumption, we obtain the hourly weather-related and illumination-related electricity consumption, and subtract this out to get residual consumption. The second stage of the model is a flexible, agent-based analytical tool that allows disaggregation of residual consumption into a sum of consumptions by different groups of appliances. This tool can be used for a variety of applications including an optimization of demand-side management and/or a development of a set of desirable patterns for schedules of electricity-related activities in households (within an acceptable range of course) in order to minimize negative effects of high peak demand.