Real Energy Consumption Research Articles

Asymmetric transfer effects among real output, energy consumption, and carbon emissions in China

This study aims to investigate nexus among China’s real output, energy consumption, and carbon emissions from 1971 to 2014. We use a nonlinear autoregressive distributed lag model to examine asymmetric transfer effects among the variables. The findings indicate that the asymmetry effect exists in the relationship among the variables in both the long and short term. Positive and negative shocks of energy consumption and carbon emissions have shown different effects on real output in sign and magnitude. Furthermore, energy consumption and carbon emissions have shown different patterns of influence on real output. In the long run, the decrease in carbon emissions has no significant impact on real output, while the decrease in energy consumption will lead to an increase in real output. In the short run, the negative shock in energy consumption in the previous periods is positively linked to real output, it means the decrease in energy consumption will lead to a decrease in real output. If the government proposes energy-conservation policies, the economic growth will be hampered in the short term but promoted in the long term. The results of this study have clear and important implications for devising energy-conservation and emission-reduction policies in China.

On CUSUM test for dynamic panel models

In this study, we consider the problem of testing for a parameter change in dynamic panel models with fixed effects. As a test, we suggest using the CUSUM test based on the score vectors and show that under regularity conditions, the test converges weakly to the supremum of a Gaussian process. The test is then compared with the location-scale CUSUM (LSCUSUM) test through Monte Carlo simulation, showing its superiority to the LSCUSUM test. We also conduct a real data analysis using the real energy consumption data of OECD countries along with their GDPs for illustration.

Double plus-zero energy historic building and improvement of hygrothermal conditions for the Palaeontology Museum of Naples

The theme of the energy requalification of historic buildings is addressed considering the Palaeontology Museum of Naples (Southern Italy). It is assessed whether, through non-invasive methods and without intervening on the building envelope, it is possible to obtain internal hygrothermal parameters suitable to allow both occupants’ thermal comfort and satisfactory conservation conditions for the archaeological finds. In addition, a relevant reduction of energy requirements and CO2 equivalent emissions is required. Dynamic energy simulations are conducted regarding both the current configuration and the various possible modifications concerning the air conditioning system; the model is calibrated by comparison with real energy consumption data. The electric and primary energy are compared for the various cases. Furthermore, the internal hygrothermal conditions for occupants’ thermal comfort and for conservation of the archaeological finds are analysed. Finally, the possibility of inserting a suitable and minimally invasive photovoltaic system is evaluated and an almost ‘Double Plus-Zero Energy Building’ is obtained.

A Novel Clustering Index to Find Optimal Clusters Size With Application to Segmentation of Energy Consumers

Increased deployment of residential smart meters has made it possible to record energy consumption data on short intervals. These data, if used efficiently, carry valuable information for managing power demand and increasing energy consumption efficiency. An efficient way to analyze these data is to first identify clusters of energy consumers, and then focus on analyzing these clusters. However deciding on the optimal number of clusters is a challenging task. In this article, we propose a clustering index that effectively finds the optimal number of clusters. The proposed index is an entropy-based measure that is obtained from eigenvalue analysis of the correlation matrix of time series of consumption data. A genetic algorithm based feature selection is used to reduce the number of features, which are then fed into clustering algorithms. We apply the proposed clustering index on two ground truth synthetic data sets and two real world energy consumption data set. The numerical simulations reveal the effectiveness of the proposed method and its superiority to a number of existing clustering indices.

Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree

Providing the user with appliance-level consumption data is the core of each energy efficiency system. To that end, non-intrusive load monitoring is employed for extracting appliance specific consumption data at a low cost without the need of installing separate submeters for each electrical device. In this context, we propose in this paper a novel non-intrusive appliance recognition system based on (i) detecting events in the aggregated power signal using a novel and powerful scheme, (ii) applying multiscale wavelet packet tree to collect comprehensive energy consumption features, and (iii) adopting an ensemble bagging tree classifier along with comparing its performance with various machine learning schemes. Moreover, to validate the proposed model, an empirical investigation is conducted on two real and public energy consumption datasets, namely, the GREEND and REDD, in which consumption readings are collected at low-frequencies. In addition, a comprehensive review of recent non-intrusive load monitoring approaches has been conducted and presented, in which their characteristics, performances and limitations are described. The proposed non-intrusive load monitoring system shows a high appliance recognition performance in terms of the accuracy, F1 score and low time complexity when it has been applied to different households from the GREEND and REDD repositories, in which every house includes various domestic appliances. Obtained results have described, e.g., that average accuracies of 97.01% and 96.36% have been reached on the GREEND and REDD datasets, respectively, which outperformed almost existing solutions considered in this framework.

The Relationship between Primary Energy Consumption and Real Gross Domestic Product: Evidence from Major Asian Countries

This study examines the relationship between primary energy consumption (PEC) and real gross domestic product (real GDP) in the top four major energy consumers in Asia, namely, China, India, Japan, and South Korea. The study period is from 1982–2018, covering 37 years of data after the second oil crisis (1979–1981). Bootstrap panel Granger causality method is applied to examine the causal relationship between PEC and real GDP. This method is capable of controlling cross-sectional dimension and cross-country heterogeneity. In addition, few studies investigate the relevance of real GDP to energy consumption, although real GDP adjusted by inflation provides an accurate picture of a country’s economic situation. Our results contribute to existing literature in the field of PEC and real GDP. Through rigorous empirical research, we derive the main conclusion as follows. The real GDP and PEC of the top four energy consumers in Asia seem to be affected by the burst of the speculative Internet bubble from 2000–2001. Therefore, this study divides the research period into three periods: 1982–2018, 1982–2001, and 2002–2018. During the 1982–2018 period, an independent causal relationship is observed between real GDP and PEC for all four countries, thus supporting the neutrality hypothesis. During the 1982–2001 period, a unidirectional causal relationship running from PEC to real GDP is observed, thus supporting the energy growth hypothesis. Moreover, the coefficient is significantly negative in India; that is, PEC constrains economic development. Thus, the Indian government should reform its energy efficiency and consumption technologies to reduce energy waste. During the 2002–2018 period, an independent causal relationship is observed between real GDP and energy consumption for all four countries, thus supporting the neutrality hypothesis. This study then changes real GDP into nominal GDP and finds a unidirectional causal relationship running from PEC to nominal GDP in South Korea, thus supporting the growth hypothesis. A unidirectional causal relationship is also observed running from nominal GDP to PEC in India, thus supporting the energy conservation hypothesis. As mentioned above, we find that the relationship between PEC and real GDP adjusted by the GDP deflator is weaker than that between PEC and nominal GDP. Nominal GDP strengthens its relationship with PEC through the effect of prices for all the goods and services produced in an economy.

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation.

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.

An Ant Colony Optimization approach for symbolic regression using Straight Line Programs. Application to energy consumption modelling

The increase of energy consumption and their direct effects on pollution and global warming have motivated governments to develop new strategies to promote a better usage of energy. One of the most important aspects related to energy efficiency is the need for a suitable model of energy consumption that can be used to make predictions or to aid experts in high level decision making processes. Symbolic regression techniques can be used to discover an energy consumption model that fits these purposes. Traditionally, the problem of symbolic regression has been solved by using genetic programming approaches to find the algebraic expression that best fits the regression problem data, where each expression is encoded as a tree structure. In previous works, we found that a different approach using Straight Line Programs as a representation technique could provide promising results for symbolic regression, although the size of the resulting algebraic expression might be increased when compared to the traditional approach. This work proposes an Ant Colony Optimization algorithm for Straight Line Programs to solve the problem, and makes a study to compare the approach with traditional genetic programming in a real energy consumption modelling problem.

Driving cycles that reproduce driving patterns, energy consumptions and tailpipe emissions

This study presents the Energy Based Micro-trip (EBMT) method, which is a new method to construct driving cycles that represent local driving patterns and reproduce the real energy consumption and tailpipe emissions from vehicles in a given region. It uses data of specific energy consumption, speed, and percentage of idling time as criteria of acceptable representativeness. To study the performance of the EBMT, we used a database of speed, fuel consumption, and tailpipe emissions (CO2, CO, and NOx), which was obtained monitoring at 1 Hz, the operation of 15 heavy-duty vehicles when they operated within different traffic conditions, during eight months. The speed vs. time data contained in this database defined the local driving pattern, which was described by 19 characteristic parameters (CPs). Using this database, we ran the EBMT and described the resulting driving cycle by 19 characteristics parameters (CPs*). The relative differences between CPs and CPs* quantified how close the obtained driving cycle represented the driving pattern. To observe tendencies of our results, we repeated the process 1000 times and reported the average relative difference (ARD) and the interquartile range (IQR) of those differences for each CP.. We repeated the process for the case of a traditional Micro-trip method and compared to previous results. The driving cycles constructed by the EBMT method showed the lowest values of ARDs and IQRs, meaning that it produces driving cycles with the highest representativeness of the driving patterns, and the best reproduction of energy consumption, and tailpipe emissions.

A smart energy consumption manager based on protues simulation

<p>In the proposed system, a strategy for the control of energy consumption by home appliances is provided. The statistics of previous energy production and consumption of a case study city are used in providing the strategy. In the design of the proposed system, home appliances are categorized into three levels of priority, even though it can be more than three. In this article the control of energy consumption is achieved using a real time energy consumption manager (ECM) based on stored data without the need for real time communication. The system which is proposed here is affordable and simple. Also, it does not require grid upgrade or power line communication through the grid. </p>

Real-world study for the optimal charging of electric vehicles

Electric Vehicles (EVs), accompanied with the use of Renewable Energy Sources (RES), are the solution for the decarbonization of the transport sector and are undoubtedly on the rise. Although EVs have been in the limelight over the last decade, little effort has been made towards the proper use of the vehicle’s battery. Therefore, a better understanding of Lithium-ion (Li-ion) batteries, since they represent the heart of the majority of electric cars, during the discharging and charging procedure is crucial. The present study, that was experimentally conducted under real-world driving conditions, quantitatively analyzes the energy losses that take place during the charging of a Battery Electric Vehicle (BEV), focusing especially in the previously unexplored 80%–100% State of Charge (SoC) area. The results show that losses, during charging within the abovementioned area, are almost double compared to the 20%–80% SoC area and vehicle’s average specific real energy consumption is almost 2 kWh/100 km more, compared to what the driver sees on the EV’s dashboard. Furthermore, it is not for the driver’s benefit to exceed 80% of SoC during charging, considering the required charging time, the distance that each SoC area provides and the life expectancy of the battery itself. Based on these results and after a thorough literature review, the authors suggest the optimum SoC range within which drivers should operate the EV’s battery.

Efficient Strategies of Static Features Incorporation into the Recurrent Neural Network

Recurrent neural networks (RNNs) have evolved to become one of the most powerful tools for making predictions on sequenced data, such as time series, textual data, signals, music etc. In many real-life cases, however, sequenced data are additionally characterized by static features which, due to their non-sequential nature, cannot be transferred directly into RNNs. In this paper, we discuss a method which incorporates static features into RNNs in order to influence and generalize the learning process. Furthermore, we will demonstrate that our approach significantly enhances the performance of RNNs, enabling the networks to learn the sequenced data exhibiting varying characteristics and then distinguish between them through the use of static supplementary information. Finally, we will evaluate our model against real energy consumption measurements of energy time series and verify that high-accuracy demand forecasts for different types of customers can be achieved only by way of incorporation of static features.

Do Emissions implied in Net Export validate the Pollution Haven Conjecture Analysis of G7 and BRICS countries

This study attempts to relook at the pollution haven hypothesis for the G7 and BRICS countries. The empirical analysis begins by detecting the cross-sectional dependence among the country panels for the period 2005-2015 and applies the second-generation unit-root and Westerlund-Edgerton LM bootstrap cointegration tests to precisely determine the long-run equilibrium relationship between emissions embodied in net export and the real GDP, FDI, trade openness, energy consumption and financial development. The result shows a 1% increase in the real GDP and the FDI increasing the emission variable for the BRICS countries by 0.210% and 0.215% respectively, however, for the G7 countries, reducing the emission variable by 0.169% and 0.038% respectively. These findings corroborate the principles of the pollution haven hypothesis, labelling the BRICS countries as pollution haven and adding new argument to restructure the mitigation obligations for the giant emitters.

Approaching the validation of building energy models: billing vs indoor environmental data

To understand the real building energy consumption, and to identify the best energy improvements (due to energy refurbishment and/or HVAC replacement), the energy audit is recommended, but it generally entails the realization of an energy model of the building itself. The main issue of this approach is the collection of data, needed to verify the reliability of the energy audit results. Currently, to validate buildings energy model retrieved by hourly semi-stationary software, is it possible to refer to: (i) bills related to energy carrier (i.e. natural gas and/or electricity); (ii) monitored indoor parameters. The aim of the paper is to understand which of the two is more suitable for the purpose. In this work, the two options are investigated considering, as case study, a family house located in central Italy, whose envelope thermophysical properties and HVAC systems are known, and whose bills and indoor environmental conditions (air temperature and relative humidity) were archived since December 2019. Results from the two validation methods are very close to each other; particularly, results show that energy audit performed with indoor environmental parameters better fits the real consumption, but it entails more complex validation procedure.

Big Data Energy Management, Analytics and Visualization for Residential Areas

With the rapid development of IoT based home appliances, it has become a possibility that home owners share with Utilities in the management of home appliances energy consumption. Thus, the proposed work empowers home owners to manage their home appliances energy consumption and allow them to compare their consumption with respect to their local community total consumption. This serves as a nudge in consumer’s behavior to schedule their home appliances operation according to their local community consumption profile and trend. Utilizing the same common communication infrastructure, it also allows the utilities on different consumption levels (community, state, country) to monitor and visualize the energy consumption in their respective grid segments on daily, monthly, and yearly basis. A high-speed distributed computing cluster based on commodity hardware with efficient big data mathematical algorithm is employed in this work. To achieve this, two big data processing paradigms are evaluated with a set of qualitative and quantitative metrics with subsequent recommendations. One million smart meter data is simulated to access individual homes. With the utilization of distributed storage and computing cluster for handling energy big data, the utilities can perform consumer load analysis and visualization on a scale of one million consumers. This helps the utilities in providing consumers a more accurate representation of how much energy they are consuming with greater granularity and with respect to their local community. Consumer and Utility centric queries are developed to create a web-based real time energy consumption management system presented in terms of dashboard charts, graphs, and reports that can be accessed by the consumer and utility providers remotely.

Prototype Development Open Source Platform-Based Electricity Meter with Minimum Rate of Change According to Module Five of the Electricity Distribution Procedure in the Brazilian National Electrical System (PRODIST)

The article proposes the development of a low-cost, well-performing open source residential energy consumption demand meter compared to commercial meters in order to achieve accurate results and remain within the limits determined by technical standards, with the objective of measuring real and instant energy consumption through microcontroller and current sensor. The prototype has the ability to provide energy consumption data for a low voltage residential load or wiring to obtain the final amount of energy consumption Data is collected in stages by collecting current and voltage, the microcontroller receives analog stimuli from sensors, as voltage and current samples are processed to calculate power, current and voltage. The use of the device leads the consumer to clarify the instantaneous energy consumption per installed component and it is possible to compare the cost benefit in relation to the energy consumption, providing consumers the follow up and a detailed view of its consumption, leading to efficient and sustainable use, enabling the improvement of the quality of energy supply to equipment connected to the distribution system.

An IoT Integrated Tool to Enhance User Awareness on Energy Consumption in Residential Buildings

Unaware behaviors of occupants can affect energy consumption even more than incorrect installations and building envelope inefficiencies, with significant overconsumptions widely documented. Real time data and an effective and frequent billing of actual consumptions are required to reach an adequate awareness of energy consumption. From this point of view, the European Directive 2012/27/EU already imposed the use of metering and sub-metering systems, setting the minimum criteria for billing and related information based on real energy consumption data. To assess the ability of buildings to exploit new information and communication technologies (ICT) and sensitize both landlords and tenants to related savings, the new European Directive 2018/844/EU promotes the use of a smart readiness indicator. At the same time, basic information about indoor thermal comfort should be also gathered, aimed at avoiding that an excessive saving tendency can determine the onset of issues related to excessively low internal temperatures. In this paper, the authors address the problem of gathering, processing, and transmitting energy consumption and basic indoor air temperature data in the framework of an Internet of Things (IoT) integrated tool aimed at increasing residential user awareness through the use of consumption and benchmark indexes. Two case-studies in which thermal and electrical energy monitoring systems have been tested are presented and discussed. Finally, the suitability of the communication of energy consumption in terms of temporal, spatial, and typological aggregation has been evaluated.

Agriculture-induced environmental Kuznets curve: the case of China.

This study investigates the long-run equilibrium relationship among carbon dioxide (CO2) emissions, real income, energy consumption, and agriculture, thus testing the existence of the agriculture-induced environmental Kuznets curve (EKC) hypothesis in the case of China for the period of 1971-2014. The level relationship among the variables in the conducted model is confirmed by the bounds test approach under the autoregressive distributed lag (ARDL) mechanism. Error correction model under the ARDL mechanism suggests that short-run values of CO2 emissions converge to its long-run equilibrium level by 73.8% speed of adjustment every year by the contribution of energy consumption, real income, and agriculture. ARDL estimation results suggest that real income and energy consumption have a positive, elastic impact; agricultural development has positive, inelastic impact on CO2 emissions where squared real income has a negative and inelastic impact on air pollution. Conditional Granger causality test results reveal that there are unidirectional causalities running from real income, squared real income, energy consumption, and agricultural development in long run as well as in the short run.

Electricity Theft Detection in Power Grids with Deep Learning and Random Forests

As one of the major factors of the nontechnical losses (NTLs) in distribution networks, the electricity theft causes significant harm to power grids, which influences power supply quality and reduces operating profits. In order to help utility companies solve the problems of inefficient electricity inspection and irregular power consumption, a novel hybrid convolutional neural network-random forest (CNN-RF) model for automatic electricity theft detection is presented in this paper. In this model, a convolutional neural network (CNN) firstly is designed to learn the features between different hours of the day and different days from massive and varying smart meter data by the operations of convolution and downsampling. In addition, a dropout layer is added to retard the risk of overfitting, and the backpropagation algorithm is applied to update network parameters in the training phase. And then, the random forest (RF) is trained based on the obtained features to detect whether the consumer steals electricity. To build the RF in the hybrid model, the grid search algorithm is adopted to determine optimal parameters. Finally, experiments are conducted based on real energy consumption data, and the results show that the proposed detection model outperforms other methods in terms of accuracy and efficiency.

Does Agriculture Matter for Environmental Kuznets Curve in Russia: Evidence from the ARDL Bounds Tests Approach

This study explores the relationship between carbon dioxide emissions and their main determinants, which include real income and energy consumption in Russia, employing data for the period 1990-2016. The hypothesis of agriculture being an important determinant of environmental quality in Russia is also tested. For estimating the short-run and long-run relationships the ARDL bounds test approach is employed in this study. The results are consistent with the Environmental Kuznets Curve (EKC) hypothesis and show that the real income and energy consumption have a statistically significant positive impact on the carbon emission and its square has a significant negative effect on the carbon emissions both in the short-run and long-run. Agricultural sector is found to be a relatively important statistically significant determinant of carbon emission in Russia as well. The pairwise Granger causality test also reveals unidirectional causality running from agriculture to the carbon emissions.