scholarly journals Prediction of Future Electric Energy Consumption using Machine Learning Framework

2020 ◽  
Vol 9 (3) ◽  
pp. 3347-3350
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Models ◽  
Electric Energy ◽  
Power Saving ◽  
Electricity Consumption ◽  
Energy System ◽  
Energy Utilization ◽  
Smart Meters ◽  
Learning Framework

In the last few years, the expanding energy utilization has imposed the formation of solutions for saving electricity. Of many solutions, one is generating a power saving policies which is defined as prediction of energy in smart environments. This model is built, based on the idea that the building residences are provided with smart meters to monitor energy consumption and can be managed accordingly. Recent prediction models focuses on performance of the prediction, but for developing a reliable energy system, it is required to predict the demand taking into account different scenarios. In this paper we propose a model for predicting future demand for energy according to different conditions using advanced machine learning framework. In this system we have a projector that builds proper state for a particular condition and using that defined state a future power demand is forecasted by the predictor. The proposed model generates utilization predictions for every 2 hours. Demonstrating the electricity consumption data for 5 years, the proposed system achieves a better performance.

scholarly journals Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4046 ◽  
Author(s):  
Sooyoun Cho ◽  
Jeehang Lee ◽  
Jumi Baek ◽  
Gi-Seok Kim ◽  
Seung-Bok Leigh
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Simulation Analysis ◽  
Residential Buildings ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Building Energy ◽  
Major Effect ◽  
Measuring Instruments ◽  
Building Energy Consumption

Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

scholarly journals Analysis of Energy System in Norway with Focus on Energy Consumption Prediction

2017 ◽  
Vol 9 (1) ◽  
pp. 5-14 ◽  
Author(s):  
Maryam Hamlehdar ◽  
Alireza Aslani
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Fossil Fuels ◽  
Renewable Energy Sources ◽  
Electricity Consumption ◽  
Energy System ◽  
High Energy ◽  
Regression Result ◽  
Industry Growth ◽  
The Status

Abstract Today, the fossil fuels have dominant share of energy supply in order to respond to the high energy demand in the world. Norway is one of the countries with rich sources of fossil fuels and renewable energy sources. The current work is to investigate on the status of energy demand in Norway. First, energy and electricity consumption in various sectors, including industrial, residential are calculated. Then, energy demand in Norway is forecasted by using available tools. After that, the relationship between energy consumption in Norway with Basic economics parameters such as GDP, population and industry growth rate has determined by using linear regression model. Finally, the regression result shows a low correlation between variables.

Simulation and Analysis of Cryogenic Air Separation Process with LNG Cold Energy Utilization

2014 ◽  
Vol 881-883 ◽  
pp. 653-658 ◽  
Author(s):  
Yong Qiang Xiong ◽  
Ben Hua
Keyword(s):  
Energy Consumption ◽  
Liquid Nitrogen ◽  
Electric Energy ◽  
Separation Process ◽  
Air Separation ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Pure Oxygen ◽  
Pure Nitrogen ◽  
Cold Energy

In this paper, a cryogenic air separation process with LNG cold energy utilization is proposed to produce liquid nitrogen and high pressure pure oxygen gas economically. To reduce the electric energy consumption of air separation products, liquid nitrogen have been produced by condensing the separated pure nitrogen gas with LNG cold energy utilization, and the recycled nitrogen is served to transfer cold energy from LNG stream to cool off air stream in the proposed cryogenic air separation process. The specifications of streams and the major equipments of the air separation process are simulated with Aspen Plus software and the main parameters analysis are performed. The results show that the energy consumption of the proposed air separation process with LNG cold energy utilization decreased about 58.2% compared with a conventional cryogenic air separation process. The compressed pressure of recycled nitrogen has a big impact on the cost of air separation products and utilization efficiency of LNG cold energy. The LNG cold energy could be fully utilized when the recycled nitrogen has been compressed to above 6.5MPa.

Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 1-20
Author(s):  
Amarasimha T. ◽  
V. Srinivasa Rao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Communication ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Faulty Node

Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

Novelty Detection in Human Behavior through Analysis of Energy Utilization

2013 ◽  
pp. 65-85 ◽  
Author(s):  
Chao Chen ◽  
Diane J. Cook
Keyword(s):  
Energy Consumption ◽  
Human Behavior ◽  
Novelty Detection ◽  
Electricity Consumption ◽  
Energy Utilization ◽  
Smart Environments ◽  
Data Sets ◽  
Smart Environment ◽  
Energy Data ◽  
Using Data

The value of smart environments in understanding and monitoring human behavior has become increasingly obvious in the past few years. Using data collected from sensors in these environments, scientists have been able to recognize activities that residents perform and use the information to provide context-aware services and information. However, less attention has been paid to monitoring and analyzing energy usage in smart homes, despite the fact that electricity consumption in homes has grown dramatically. In this chapter, the authors demonstrate how energy consumption relates to human activity through verifying that energy consumption can be predicted based on the activity that is being performed. The authors then automatically identify novelties in human behavior by recognizing outliers in energy consumption generated by the residents in a smart environment. To validate these approaches, they use real energy data collected in their CASAS smart apartment testbed and analyze the results for two different data sets collected in this smart home.

scholarly journals A machine learning framework to determine geolocations from metagenomic profiling

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Lihong Huang ◽  
Canqiang Xu ◽  
Wenxian Yang ◽  
Rongshan Yu
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Geographic Origin ◽  
Training Data ◽  
Metagenomic Data ◽  
Training Dataset ◽  
Kriging Interpolation ◽  
Learning Framework ◽  
Testing Data ◽  
Microbial Samples

Abstract Background Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples’ geolocations. In this paper, we present a machine learning framework to determine the geolocations from metagenomics profiling of microbial samples. Results Our method was applied to the multi-source microbiome data from MetaSUB (The Metagenomics and Metadesign of Subways and Urban Biomes) International Consortium for the CAMDA 2019 Metagenomic Forensics Challenge (the Challenge). The goal of the Challenge is to predict the geographical origins of mystery samples by constructing microbiome fingerprints.First, we extracted features from metagenomic abundance profiles. We then randomly split the training data into training and validation sets and trained the prediction models on the training set. Prediction performance was evaluated on the validation set. By using logistic regression with L2 normalization, the prediction accuracy of the model reaches 86%, averaged over 100 random splits of training and validation datasets.The testing data consists of samples from cities that do not occur in the training data. To predict the “mystery” cities that are not sampled before for the testing data, we first defined biological coordinates for sampled cities based on the similarity of microbial samples from them. Then we performed affine transform on the map such that the distance between cities measures their biological difference rather than geographical distance. After that, we derived the probabilities of a given testing sample from unsampled cities based on its predicted probabilities on sampled cities using Kriging interpolation. Results show that this method can successfully assign high probabilities to the true cities-of-origin of testing samples. Conclusion Our framework shows good performance in predicting the geographic origin of metagenomic samples for cities where training data are available. Furthermore, we demonstrate the potential of the proposed method to predict metagenomic samples’ geolocations for samples from locations that are not in the training dataset.

scholarly journals Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

2021 ◽  
Author(s):  
Diego P. Pinto-Roa ◽  
Hernán Medina ◽  
Federico Román ◽  
Miguel García-Torres ◽  
Federico Divina ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Electric Energy ◽  
Electricity Consumption ◽  
Search Space ◽  
Consumption Pattern ◽  
Electric Energy Consumption ◽  
System A ◽  
Time Period ◽  
Computation Scheme ◽  
Significant Patterns

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

scholarly journals Electric energy consumption of milking process on some Finnish dairy farms

2017 ◽  
Vol 26 (3) ◽  
Author(s):  
Mari Rajaniemi ◽  
Tapani Jokiniemi ◽  
Laura Alakukku ◽  
Jukka Ahokas
Keyword(s):  
Energy Consumption ◽  
Energy Savings ◽  
Electric Energy ◽  
Cooling Water ◽  
Electricity Consumption ◽  
Dairy Farms ◽  
Energy Costs ◽  
Heating Systems ◽  
Electric Energy Consumption ◽  
Energy Saving Potential

The aim of this study was to examine the electric energy consumption of milking process on dairy farms and to evaluate the methods to improve the energy efficiency. The electricity consumption of the milking process was measured on three dairy farms in Southern Finland, and it varied between 37–62 Wh kg-1 milk.  The largest energy saving potential was identified in milk cooling and the heating of cleaning water. Even simple methods, such as placing the condenser of the refrigeration system outside, may reduce the energy consumption of milk cooling by 30%. Efficient milk pre-cooling can reduce the energy consumption of the whole milking process by more than 25%. Even larger energy savings are possible with a sophisticated milk cooling – water heating systems. It was concluded that there is a significant potential to reduce the energy consumption and energy costs of the milking process, and thus to improve the profitability and sustainability of the sector at the same time.

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

scholarly journals Load Forecasting of an Optimized Green Residential System Using Machine Learning Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 22
Author(s):  
Nabeel Zahoor ◽  
Irfan Ullah ◽  
Abid Ali Dogar ◽  
Burhan Ahmed
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Management ◽  
Learning Algorithm ◽  
Renewable Energy Sources ◽  
Load Forecasting ◽  
Energy Utilization ◽  
Peak Time ◽  
Time Data ◽  
Micro Grid

Load forecasting of a micro-grid system has become a challenging task due to its high volatile nature and uncertainty. Residential energy consumption is one of the most talked-about and confusing topics among different electricity loads in terms of future information and is mainly affected by irregular human activity and changing weather conditions. Therefore, techniques and algorithms are needed to reduce energy consumption and enhance the smartness of the system. Load forecasting of an optimized residential system using a machine learning (ML) algorithm is proposed for an islanded green residential system. The load profile of residential electricity consumption is developed by real-time data collected. Photovoltaic (PV) and wind energy (WE) units are considered renewable energy sources in batteries to entertain the residential loads in the proposed prototype. An efficient energy management system (EMS) is introduced to create a balance between power generation and consumption with the help of intelligent appliances under a controlled framework and to overcome peak time consumption. Prediction of load and proper energy utilization are presented to ensure the stability and durability of the system. For efficient micro-grid energy management, the residential load is forecasted using a ML algorithm named non-linear autoregressive exogenous (NARX) neural network (NN) with a minute mean absolute percentage square error of 0.226% which is far less than that of previous work performed in different forecasting scenarios. As a result, an efficient model is designed for a standalone DC micro-grid.

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