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.

scholarly journals Hourly Prediction of Building Energy Consumption: An Incremental ANN Approach

2017 ◽  
Vol 2 (7) ◽  
pp. 27 ◽  
Author(s):  
Aulon Shabani ◽  
Orion Zavalani
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Management ◽  
Learning Algorithm ◽  
Building Energy ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Improve Energy Efficiency ◽  
Batch Sizes ◽  
Research Domain

Rapid growth of buildings energy consumption puts the focus to improve energy efficiency by building engineers and operators. Energy management through forecasting approaches using machine-learning algorithms is an increasing research domain. Most of algorithms focus on predicting energy consumption when a considerable amount of past-observed data exist.  In this paper, we focus on the case when small amount of available data exist and the amount of data increases incrementally by time. Artificial Neural Networks used as the learning algorithm take as the training data mini batches of different sizes. Algorithm is evaluated on different batch sizes and compared to baseline learner.

scholarly journals Energy management strategies for combined heat and electric power micro-grid

2016 ◽  
Vol 20 (4) ◽  
pp. 1091-1103 ◽  
Author(s):  
Marina Barbaric ◽  
Drazen Loncar
Keyword(s):  
Energy Management ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Optimization Technique ◽  
Management Strategies ◽  
Distribution Networks ◽  
Energy Storage System ◽  
Optimization Approach ◽  
Micro Grid ◽  
Additional Rule

The increasing energy production from variable renewable energy sources such as wind and solar has resulted in several challenges related to the system reliability and efficiency. In order to ensure the supply-demand balance under the conditions of higher variability the micro-grid concept of active distribution networks arising as a promising one. However, to achieve all the potential benefits that micro-gird concept offer, it is important to determine optimal operating strategies for micro-grids. The present paper compares three energy management strategies, aimed at ensuring economical micro-grid operation, to find a compromise between the complexity of strategy and its efficiency. The first strategy combines optimization technique and an additional rule while the second strategy is based on the pure optimization approach. The third strategy uses model based predictive control scheme to take into account uncertainties in renewable generation and energy consumption. In order to compare the strategies with respect to cost effectiveness, a residential micro-grid comprising photovoltaic modules, thermal energy storage system, thermal loads, electrical loads as well as combined heat and power plant, is considered.

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.

Contemporary Energy Management Systems and Future Prospects

2021 ◽  
pp. 1984-2013
Author(s):  
Amir Manzoor
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
User Participation ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Systems ◽  
Research Areas ◽  
Emerging Trends

The transformation of electric grid into smart grid has improved management of available resources and increased energy efficiency. Energy management systems (EMS) play an important role in enhancing user participation in control of energy management. Using such systems, consumers can obtain information about their energy consumption patterns and shape their energy consumption behaviors for efficient energy utilization. Contemporary EMS utilizes advanced analytics and ICT to provide consumers actionable feedback and control of energy management. These systems provide high availability, an easy-to-use user interface, security, and privacy. This chapter explores the contemporary EMS, their applications, classifications, standards, and frameworks. The chapter defines a set of requirements for EMS and provides feature comparison of various EMS. The chapter also discusses emerging trends and future research areas in EMS.

scholarly journals Short-Term Electric Load Forecasting Using Standardized Load Profile (SLP) And Support Vector Regression (SVR)

2019 ◽  
Vol 9 (4) ◽  
pp. 4548-4553
Author(s):  
N. T. Dung ◽  
N. T. Phuong
Keyword(s):  
Machine Learning ◽  
Electricity Market ◽  
Business Strategy ◽  
Learning Algorithm ◽  
Load Forecasting ◽  
Support Vector ◽  
Forecast Errors ◽  
Short Term ◽  
Load Profile ◽  
Short Term Forecasting

Short-term load forecasting (STLF) plays an important role in business strategy building, ensuring reliability and safe operation for any electrical system. There are many different methods used for short-term forecasts including regression models, time series, neural networks, expert systems, fuzzy logic, machine learning, and statistical algorithms. The practical requirement is to minimize forecast errors, avoid wastages, prevent shortages, and limit risks in the electricity market. This paper proposes a method of STLF by constructing a standardized load profile (SLP) based on the past electrical load data, utilizing Support Regression Vector (SVR) machine learning algorithm to improve the accuracy of short-term forecasting algorithms.

scholarly journals Modeling and Optimizing a Chiller System Using a Machine Learning Algorithm

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2860 ◽  
Author(s):  
Jee-Heon Kim ◽  
Nam-Chul Seong ◽  
Wonchang Choi
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Prediction Accuracy ◽  
Learning Algorithm ◽  
Training Data ◽  
Machine Learning Algorithm ◽  
Air Conditioning System ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Input Variables

This study was conducted to develop an energy consumption model of a chiller in a heating, ventilation, and air conditioning system using a machine learning algorithm based on artificial neural networks. The proposed chiller energy consumption model was evaluated for accuracy in terms of input layers that include the number of input variables, amount (proportion) of training data, and number of neurons. A standardized reference building was also modeled to generate operational data for the chiller system during extended cooling periods (warm weather months). The prediction accuracy of the chiller’s energy consumption was improved by increasing the number of input variables and adjusting the proportion of training data. By contrast, the effect of the number of neurons on the prediction accuracy was insignificant. The developed chiller model was able to predict energy consumption with 99.07% accuracy based on eight input variables, 60% training data, and 12 neurons.

Contemporary Energy Management Systems and Future Prospects

2016 ◽  
pp. 265-292
Author(s):  
Amir Manzoor
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
User Participation ◽  
Energy Utilization ◽  
Security And Privacy ◽  
Management Systems ◽  
Future Research ◽  
Energy Management Systems ◽  
Research Areas ◽  
Emerging Trends

The transformation of electric grid into smart grid has improved management of available resources and increased energy efficiency. Energy management systems (EMS) play an important role in enhancing user participation in control of energy management. Using such systems, consumers can obtain information about their energy consumption patterns and shape their energy consumption behaviors for efficient energy utilization. Contemporary EMS utilizes advanced analytics and ICT to provide consumers actionable feedback and control of energy management. These systems provide high availability, an easy-to-use user interface, security, and privacy. This chapter explores the contemporary EMS, their applications, classifications, standards, and frameworks. The chapter defines a set of requirements for EMS and provides feature comparison of various EMS. The chapter also discusses emerging trends and future research areas in EMS.

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