Variation In Energy Consumption Research Articles

Strategies for predictive power: Machine learning models in city-scale load forecasting

This study focuses on enhancing machine learning (ML) algorithms' performance in predicting daily loads for Kirkuk, Iraq—an essential element in energy planning, resource allocation, and policymaking. We explore single and ensemble learning algorithms, including AdaBoost, Bagging, Support Vector Regression (SVR), and Decision Tree (DT). To assess accuracy, we employ critical metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared (R2). MSE and RMSE gauge precision through squared differences between predictions and actual energy consumption. MAPE reveals percentage deviations, while R2 quantifies model fit, indicating its ability to capture energy consumption variance. Our results highlight Bagging's superiority, particularly in day-ahead forecasts, demonstrating superior accuracy over Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). These findings underscore the importance of feature reduction methods in enhancing performance. Bagging exhibits promise across various feature reduction scenarios, proficiently predicting energy consumption. Furthermore, the AdaBoost algorithm demonstrates commendable performance. The application of voting ensemble learning emerges as a particularly insightful approach, effectively reducing the squared differences and deviations in energy consumption forecasts. The significant implications of these findings suggest that the models, with their impressive performance, could serve as valuable tools for energy planners and policymakers in Kirkuk, playing a key role in optimizing resource allocation for efficient energy utilization.

The spatial analysis of green economy indicators of OECD countries

The present study aims to examine the effect of the geographical location relationship between economic growth and environmental pollution. For this purpose, the spatial relationship between the variable CO2 emission and the variables energy consumption (ENC), real GDP per capita (GDP), urbanization rate (URB), and trade liberalization (DAE) was investigated by using the data of 37 OECD countries for the period of 1990–2015. The geographical location relationship was determined by using LISA (Local Indicators of Spatial Association) analysis, which is one of the spatial autocorrelation analysis methods. Spatial distribution maps were prepared. Considering the years determined according to Moran I index results, a gradually increasing positive autocorrelation was found for CO2 and ENC variables and a low increasing positive correlation for DAE and GDP variables. For the variable URB, a low increasing positive autocorrelation was found for the year 1990 and a high increasing positive autocorrelation for the year 2015. Then, using the LISA clustering maps, the relationships between the countries were clustered as low, high, and non-related. As a result of this study, given the spatial analysis results, the effect of energy consumption on the carbon emission was found to be positive in general. Increases in trade liberalization increased carbon emissions in some countries and decreased it in some others. On the other hand, increases in the urbanization rate decreased carbon emissions in some countries and had a positive effect in some others. The trade openness index was found to have a generally negative effect on the carbon emission. Within the scope of this study, Spatial Regression Analysis was conducted separately for the years 1990 and 2015. In this analysis, CO2 is the dependent variable, whereas ENC, GDP, URB, and DAE are independent variables. Given the results of spatial regression analysis, it was found that ENC, GDP, and DAE variables have a positive relationship with the CO2 variable. It was determined that there was no significant relationship between URB and CO2. Considering the results achieved, it could be possible to observe the increasing and decreasing effects of variables, which were examined here, on the CO2 emissions.

The relationship between climate risk, climate policy uncertainty, and CO2 emissions: Empirical evidence from the US

This paper examines the relationship between climate risk and climate policy uncertainty, and CO2 emissions in the US over the 2000–2022 period using a structural Factor- Augmented Vector AutoRegression (FAVAR) model with a two-step principal component analysis based on monthly observations. We employ a very recent measure to proxy for uncertainty regarding climate policy based on the Climate Policy Uncertainty Index (CPU) of Gavriilidis (2021), while Climate Risk is proxied by financial cost of natural disasters and number of deaths. We use different variables for CO2 emissions, based on total and sectoral emission (commercial, electric power, residential sector, transportation, and industrial sector). The results indicate that a significant percentage of the variance of CO2 emissions in the US, is explained by Natural Disasters Cost, which also seem to account for a significant percentage of the US Total Renewable Energy Consumption variance. Shocks to disaster costs seem to decrease all type of emissions significantly and also increase renewable energy use significantly. Natural disasters increase political disagreement among U.S. politicians, as well as, the climate policy uncertainty, highlighting the need for efficient policymaking and regulations. In further results, we find that an increase in Partisan Conflict decreases emissions and explains a significant amount of renewable energy variance.

A remote consultation system for sports injury based on wireless sensor network

INTRODUCTION: Although current research methods can realize the effective collection of human physiological signals in the health monitoring system, they cannot obtain the ideal detection effect due to the influence of the communication performance in the health monitoring system. OBJECTIVES: In order to improve the monitoring performance of remote consultation, a sports injury remote consultation system based on wireless sensor network is designed. METHODS: The wearable sensors is used in the body area network to collect human physiological signals. Through the wireless sensor network of the wireless communication module, the collected human physiological signals are transmitted to the remote consultation module. The wireless communication module selects CC2530 chip as the core chip of the wireless communication module. A fixed partition routing algorithm based on energy balance is used to stably transmit human physiological signals. RESULTS: The consultation personnel of the remote consultation module make a sports injury consultation judgment based on the received physiological signal results of the human body. The system test results show that the designed system can accurately monitor various physiological indicators of the human body. The wireless sensor network energy consumption of the system in this paper is all less than 500J, the energy consumption variance of the cluster head is less than 4×10-3, and the number of surviving nodes can be guaranteed to be higher than 130. It has high communication performance of wireless sensor network. CONCLUSION: The system can accurately judge whether there is a sports injury according to the monitoring results of physiological indicators, and realize the effective consultation of sports injury.

Forecasting and identifying the meteorological and hydrological conditions favoring the occurrence of severe hazes in Beijing and Shanghai using deep learning

Abstract. Severe haze or low-visibility events caused by abundant atmospheric aerosols have become a serious environmental issue in many countries. A framework based on deep convolutional neural networks containing more than 20 million parameters called HazeNet has been developed to forecast the occurrence of such events in two Asian megacities: Beijing and Shanghai. Trained using time-sequential regional maps of up to 16 meteorological and hydrological variables alongside surface visibility data over the past 41 years, the machine has achieved a good overall performance in identifying haze versus non-haze events, and thus their respective favorable meteorological and hydrological conditions, with a validation accuracy of 80 % in both the Beijing and Shanghai cases, exceeding the frequency of non-haze events or no-skill forecasting accuracy, and an F1 score specifically for haze events of nearly 0.5. Its performance is clearly better during months with high haze frequency, i.e., all months except dusty April and May in Beijing and from late autumn through all of winter in Shanghai. Certain valuable knowledge has also obtained from the training, such as the sensitivity of the machine's performance to the spatial scale of feature patterns, that could benefit future applications using meteorological and hydrological data. Furthermore, an unsupervised cluster analysis using features with a greatly reduced dimensionality produced by the trained HazeNet has, arguably for the first time, successfully categorized typical regional meteorological–hydrological regimes alongside local quantities associated with haze and non-haze events in the two targeted cities, providing substantial insights to advance our understandings of this environmental extreme. Interesting similarities in associated weather and hydrological regimes between haze and false alarm clusters or differences between haze and missing forecasting clusters have also been revealed, implying that factors, such as energy-consumption variation and long-range aerosol transport, could also influence the occurrence of hazes, even under unfavorable weather conditions.

Machine learning prediction models for battery-electric bus energy consumption in transit

Road gradient (g), initial state of charge (SoCi), drag coefficient (Cd), road condition (RC), HVAC, driver aggressiveness (Dagg.), passenger loading (PL), stop density (SD), average speed (Va), Multiple Regression Analysis (MLR), Radial Basis Function (RBF), Decision Tree Model (DT), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), Multilayer Perception Neural Network (MLP-NN), and Radial Basis Neural Network (RBNN) • We developed and validated a simulation model to estimate the E C of BEBs. • A factorial experimental design generated BEB operation scenarios. • Seven data-driven models are developed to predict BEB energy consumption. • The models accommodate vehicular, operational, topological, and external parameters. • Multiple Regression and support vector machine models are deemed appropriate to predict BEB’s E C . The energy consumption (E C ) of battery-electric buses (BEB) varies significantly due to the intertwined relationships of vehicular, operational, topological, and external parameters. This variation is posing several challenges to predict BEB’s energy consumption. Several studies are calling for the development of data-driven models to address this challenge. This study develops and compares seven data-driven modelling techniques that cover both machine learning and statistical models. The models are based on a full-factorial experimental design ( n = 907,199 ) of a validated Simulink energy simulation model. The models are then used to predict E C using a testing dataset ( n = 169,344 ). The results show some minor discrepancies between the developed models. All models explained more than 90% of the energy consumption variance. Further, the results indicate that road gradient and the battery state of charge are the most influential factors on E C , while driver behaviour and drag coefficient have the lowest impact.

Use of Renewable Energy as a Strategic Action for Companies in the Agricultural Sector

Objective: To describe the use of renewable energies in companies in theagricultural sector in order to associate their use to competitiveness variables and todetermine if their incorporation has an impact on the competitive performance of thecompanies.Design/Methodology/Approach: An instrument was designed aimed at managersand owners of companies in the agricultural sector based on the theory of industrialorganization and competitiveness. The information was coded, and association testsas well as comparative cluster analysis were performed.Results: The use of renewable energy by the companies addresses economic andcommercial strategies. Of the twelve competitiveness variables used to comparecompanies, nine were linked to the use of renewable energies. The companies usingrenewable energy demonstrated superior results in competitiveness.Study Limitations/Implications: The results are applicable to companies in theagricultural sector within the region analyzed. Financial and energy consumptionvariables should be considered if the method of analysis is to be replicated.Findings/Conclusions: The use of renewable energy is a strategic behavior thatallows companies to improve their market position. Companies that use renewableenergy as a strategic behavior are more competitive.

PSO-based sink placement and load-balanced anycast routing in multi-sink WSNs considering compressive sensing theory

This paper deals with the sink placement and anycast routing to increase the lifetime of multi-sink wireless sensor networks. Two algorithms are proposed, namely “Multi-sink Placement and Anycast Routing (MPAR)” and “Extended Multi-sink Placement and Anycast Routing (EMPAR)”, to jointly address the problems of clustering, multi-sink placement, and load-balanced anycast routing. MPAR and EMPAR rely on a two-level architecture in which sensors are clustered at the lower level. Each sensor transmits its data to the corresponding Cluster Head (CH) via a load-balanced data aggregation routing tree. At the upper level, both schemes use a modified particle swarm optimization algorithm to determine the best location of sinks. For each sink, a high-level anycast routing tree is developed using the ant colony optimization algorithm. Each anycast tree uses the hybrid Compressive Sensing (CS) method to forward the aggregated data from CHs to sinks. Extensive simulations are conducted to illustrate the efficiency of the proposed algorithms in terms of energy consumption, energy consumption variance, and network lifetime. The results show that EMPAR has a better performance than MPAR due to its CH selection strategy. As an advantage, EMPAR considers both remaining energy and distance criteria along with a rest factor to select the best CH for each cluster. For an average number of clusters, EMPAR reduces the energy consumption by 5.98% and 12.20%, respectively, compared to the MPAR algorithm and the energy-aware CS-based data aggregation algorithm. It also increases the network lifetime in comparison with the aforementioned algorithms by 12.26% and 30.38%, respectively.

Routing optimization strategy of IoT awareness layer based on improved cat swarm algorithm

With the rapid development of Internet of Things technology, wireless sensor networks have been widely used in many places. This study mainly discusses the routing optimization strategy of the IoT perceptive layer based on the improved cat swarm algorithm. This study simulates a perceptive network with 100 nodes deployed randomly. As SDWSN for Internet of Things applications, in order to simulate the data transmission requirements of IoT communication and ensure the fairness of experimental comparison, this study uses the pseudo-random mechanism to generate the source address and destination address of data packets. A special SDN controller node is added to the network. The SDN controller node broadcasts information to each sensing node, and the common sensing node sends node information to the SDN controller. The SDN controller can survive the global time graph of the entire network according to the information of the common node. In order to avoid the problem of high energy consumption of cluster heads caused by long-distance data transmission, the cat algorithm protocol adopts multi-hop communication between cluster heads and BS and uses network overhead index to quantify link overhead as the basis for cluster heads to select the next hop node. When the inter-cluster multi-hop route is successfully established, the wireless sensor node begins to collect data and send it to BS node. Six monitoring nodes, two coordinators and one workstation were selected as the test objects. The data volume sent by each node was 2000, and the accuracy rate of test transmission information at different rates and transmission distances was determined. The group network coverage rate of cat swarm algorithm is always above 95%, and the average energy loss of nodes is the highest and less than 36%. The results show that the aggregate of energy consumption of cluster heads and the variance of energy consumption are the lowest in the improved cat cluster algorithm, which ensures the reliable transmission of node data.

Load‐balanced data gathering in Internet of Things using an energy‐aware cuckoo‐search algorithm

SummaryThis paper deals with the lifetime problem in the Internet of Things. We first propose an efficient cluster‐based scheme named “Cuckoo‐search Clustering with Two‐hop Routing Tree (CC‐TRT)” to develop a two‐hop load‐balanced data aggregation routing tree in the network. CC‐TRT uses a modified energy‐aware cuckoo‐search algorithm to fairly select the best cluster head (CH) for each cluster. The applied cuckoo‐search algorithm makes the CH role to rotate between different sensors round by round. Subsequently, we extend the CC‐TRT scheme to present two methods for constructing multi‐hop data aggregation routing trees, named “Cuckoo‐search Clustering with Multi‐Hop Routing Tree (CC‐MRT)” and “Cuckoo‐search Clustering with Weighted Multi‐hop Routing Tree (CC‐WMRT).” Both CC‐MRT and CC‐WMRT rely on a two‐level structure; they not only use an energy‐aware cuckoo‐search algorithm to fairly select the best CHs but also adopt a load‐balanced high‐level routing tree to route the aggregated data of CHs to the sink node. However, CC‐WMRT slightly has a better performance thanks to its low‐level routing strategy. As an advantage, the proposed schemes balance the energy consumption among different sensors. Numerical results show the efficiency of the CC‐TRT, CC‐MRT, and CC‐WMRT algorithms in terms of the number of transmissions, remaining energy, energy consumption variance, and network lifetime.

Data Collection Algorithm of a 3D Wireless Sensor Network That Weighs Node Coverage Rate and Lifetime

Considering the movement of a sink node in this study to solve the data transmission problem of sensor nodes in a 3D network environment, we propose a Data Collection Algorithm of a 3D wireless sensor network (DCA_3D) that weighs node coverage rate and lifetime. DCA_3D establishes a data collection optimization model that weighs node coverage rate and lifetime with the constraints of the sink node’s moving path selection, data flow, energy consumption, and link transmission. Subsequently, DCA_3D calculates the fitness value of the sink node’s moving path by solving the data collection optimization model with the known sink node’s moving path. Then it uses a modified artificial bee colony algorithm to solve the moving path selection problem of the sink node, and finally obtains the optimal scheme. In the scheme, sink node can find the optimal moving path, whereas the sensor node can find the optimal data communication path. The simulation results show that regardless of the moving path length of the sink node, the maximum data collection hops of the sink node and the number of static sensor nodes change, DCA_3D can find the optimal moving path of the sink node. It can improve the coverage rate of the sensor nodes, the network lifetime and average data transmission amount, and reduce the average energy consumption variance and the average packet loss rate. DCA_3D outperforms the state-of-arts such as RAND, GREED, EDG_3D, and ANT.

Energy consumption by rural migrant workers and urban residents with a hukou in China: quality-of-life-related factors and built environment

This paper compares energy consumption (electricity, gasoline, and gas) by rural migrant workers and urban residents with a hukou (a China-specific household registration system) and influential factors (including quality-of-life [QOL]-related factors, built environment, and individual and household attributes) in China. A questionnaire survey was conducted in Dalian (a coastal city) in 2014 and in Guiyang (an inland city) in 2015, respectively. A zero-inflated negative binomial (ZINB) model was applied to understand whether and how much people consume a certain type of energy. The results showed that built environment explains 8.4–21.8% and 6.3–41.4% of the total variance in energy consumption by rural migrant workers and urban residents with a hukou. The corresponding variance related to QOL-related factors was 9.1–15.8% and 4.1–22.6%, respectively. The built environment was mostly associated with electricity consumed by urban residents with a hukou, while its influences on other types of energy consumption were moderate. Mixed effects of both built environment and QOL-related factors on reducing energy consumption were observed. Thus, it is context-sensitive whether and how much compact city development and social security policy affect residents’ energy consumption.

On the performance of sink placement in WSNs considering energy-balanced compressive sensing-based data aggregation

This paper aims to address the problem of network lifetime in wireless sensor networks. Our analysis jointly considers the cluster-based routing, Compressive Sensing (CS) theory, load-balancing, and sink placement. We first provide a formulation to theoretically study the impact of the sink placement on the number of transmissions and the network lifetime. Then, we propose two data collection methods named “Energy-aware CS-based Data Aggregation (ECDA)” and “Energy-balanced High level Data aggregation Tree (EHDT)”. The proposed algorithms not only balance the energy consumption among different nodes, but also increase the network lifetime. As an advantage, both schemes are compatible with constant and mobile sinks. In this regard, considering the CS theory, we discuss the impact of the sink location and show the role of the sink mobility on the network performance. Although both algorithms improve the network lifetime, EHDT further considers the load-balancing problem in the network. It applies the weighted routing trees for transmitting data projections from the cluster heads to the sink and fairly distributes the energy consumption among different nodes. Numerical results confirm the efficiency of the proposed algorithms in terms of the number of transmissions, the energy consumption, the energy consumption variance, and the network lifetime.

Toward QoI and Energy Efficiency in Participatory Crowdsourcing

Today's smartphones are also fundamentally transforming the traditional understanding of “crowdsourcing” to an emerging type of participatory task-oriented application. It aims to support the so-called “Citizen Science efforts” for knowledge discovery to understand human behavior and measure/evaluate their opinions. To facilitate these scenarios, in this paper, we propose a novel efficient network management framework for participatory crowdsourcing. Specifically, we first formulate the optimization problem and propose a closed-form optimal solution to meet the quality-of-information (QoI) requirements of the task while minimizing the energy consumption variance among participants. We then largely extend the traditional framework of Gur Game for distributed decision making to recommend different levels of information contribution for each participant by merging multiple automaton chains into a single chain with multiple steady states. By modeling the user bidding behaviors, we propose a few incentive-based participant selection schemes to maximize the platform's benefits and meet participants' expectations. We extensively evaluate the proposed schemes under the Massachusetts Institute of Technology (MIT) Social Evolution data set, where both QoI requirements of the request and credit saving are successfully achieved, with a satisfactory level of energy consumption fairness among participants.

TProf: An energy profiler for task-parallel programs

We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS).

Energy-based optimization of the material stock allowance for turning-grinding process sequence

Intensive appeal for energy saving has driven the production industry towards more environmentally benign manufacturing. Diverse process settings in manufacturing can result in a high variance of energy consumption. Regarding process chains, the allocation of machining stock to each process is very crucial in terms of energy use. Optimization of material stock allowance and process parameters can effectively reduce the production energy. In this study, two cylindrical machining processes—turning and grinding—are compared at machine level with respect to energy intensity and surface roughness. A systematic energy-efficient approach, based on minimizing the production energy while complying with the required surface roughness, has been developed and validated to optimize the grinding stock for the turning-grinding process sequence. Compared to conventional stock allocation method, the developed approach results in more than 16 % of energy reduction for the turning-grinding process.

Demand Bidding Program and Its Application in Hotel Energy Management

Demand bidding program (DBP) is recently adopted in practice by some energy operators. DBP is a risk-free demand response program targeting large energy consumers. In this paper, we consider DBP with the application in hotel energy management. For DBP, optimization problem is formulated with the objective of maximizing expected reward, which is received when the the amount of energy saving satisfies the contract. For a general distribution of energy consumption, we give a general condition for the optimal bid and outline an algorithm to find the solution without numerical integration. Furthermore, for Gaussian distribution, we derive closed-form expressions of the optimal bid and the corresponding expected reward. Regarding hotel energy, we characterize loads in the hotel and introduce several energy consumption models that capture major energy use. With the proposed models and DBP, simulation results show that DBP provides economics benefits to the hotel and encourages load scheduling. Furthermore, when only mean and variance of energy consumption are known, the validity of Gaussian approximation for computing optimal load and expected reward is also discussed.

Optimal Energy Routing Protocol Algorithm in Wireless Network

As some nodes in the wireless network are supplied by the battery, energy is critical to data transmission, to optimize the energy consumption of the wireless network and prolong the network lifetime, the optimal energy routing protocol algorithm is proposed. The algorithm of this text uses threshold energy and average residuary energy to balance the energy consumption, uses network coding to reduce the energy consumption, unify them to optimize the network energy effectively. Finally the comparative simulation experiment between this algorithm and COPE, DSR algorithm is conducted in the variance of energy consumption in unit time, energy consumption in unit bit and residuary energy, the result shows the energy optimization routing protocol algorithm this text puts forward can reduce the transmission energy consumption and balance the network energy allocation

Methodology to Measure and Verify The Impacts Obtained from Energy Efficiency Activities

The necessity for effective and sustainable energy efficiency is now, more than ever, of critical importance for the various industrial sectors within the South African economy. The critical shortage of electricity, rising electricity costs, the Energy Efficiency Accord, the proposed Power Conservation Program (PCP), and tax incentives are but a few of the primary driving forces behind energy efficiency in South Africa's energy market. Electricity and its production cost is a risk that needs to be managed, which will effectively force industry to drive energy efficiency within itself. However, the impacts of energy efficiency often dissipate over time due to the lack of effective management information, which can only be provided through thorough measurement and verification (MV but it is also extremely important to prove performance in order to benefit from the government's proposed tax incentives for energy efficiency. Measurement and verification can effectively isolate the energy efficiency impacts of an industrial site from the variance in energy consumption due to numerous other factors, thus providing the ability to quantify and verify energy efficiency impacts in an objective, accurate, and transparent manner based on international standards.

Analysis of energy consumption patterns in multi-family housing in a moderate cold climate

This paper analyzes the energy consumption during the period 2001–2006 of 192 flats distributed in three-storey buildings, in order to understand how current policies related to energy use could be improved for increased residential energy efficiency in Argentina. The buildings (1, 2 and 3 bedrooms) are located in La Pampa, central Argentina, in a moderate cold climate. The dimensional and energy-consumption variables are studied (area, envelope's area, FAEP=envelope's area/floor area; envelope's thermal resistance R, volumetric heat loss G and auxiliary heating Q aux). The natural gas consumption is analyzed at annual and seasonal levels. Consumption variability among buildings, storeys and flats is calculated. The quantitative analysis is coupled to a qualitative description through direct observation of the buildings. The results show: (a) a high incidence of natural gas consumption in the total annual energy consumption (natural gas+electricity), (b) seasonality of natural gas consumption, with a maximum value in the cold period July–August (variability=80%), (c) little variability among buildings of the annual natural gas consumption (4.17%), (d) the lowest average energy consumption at the first floor, (e) high variability among flats on each storey, (f) winter consumption of a multi-family dwelling lower than a single-family dwelling of similar area and (g) little seasonal variability of the electricity consumption.