Machine Learning-Based Day-Ahead Market Energy Usage Bidding for Smart Microgrids

In-silico research has grown considerably. Today?s scientific code involves long-running computer simulations and hence powerful computing infrastructures are needed. Traditionally, research in high-performance computing has focused on executing code as fast as possible, while energy has been recently recognized as another goal to consider. Yet, energy-driven research has mostly focused on the hardware and middleware layers, but few efforts target the application level, where many energy-aware optimizations are possible. We revisit a catalog of Java primitives commonly used in OO scientific programming, or micro-benchmarks, to identify energy-friendly versions of the same primitive. We then apply the micro-benchmarks to classical scientific application kernels and machine learning algorithms for both single-thread and multi-thread implementations on a server. Energy usage reductions at the micro-benchmark level are substantial, while for applications obtained reductions range from 3.90% to 99.18%.

Download Full-text

Energy Meter Data Analysis Using Machine Learning Techniques

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc.v8i6.5409 ◽

2020 ◽

Vol 8 (6) ◽

pp. 08-13

Author(s):

Anitha Kumari K ◽

Indusha M ◽

Abarna Devi D ◽

Dheva Dharshini S

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Use Cases ◽

Machine Learning Techniques ◽

Energy Usage ◽

Energy Meter ◽

Learning Techniques ◽

Significant Interest ◽

F Measure

With the advancement of technology, existence of energy meters are not merely to measure energy units. The proliferation of energy meter deployments had led to significant interest in analyzing the energy usage by the machines. Energy meter data is often difficult to analyzeowing to the aggregation of many disparate and complex loads. At utility scales, analysis is further complicated by the vast quantity of data and hence industries turn towards applying machine learning techniques for monitoring and measuring loads of the machines. The energy meter data analysis aims at analyzing the behavior of the machine and providing insights on usage of the energy. This will help the industries to identify the faults in the machine and to rectify it.Two use cases with two different motor specifications is considered for evaluation and the efficiency is proved by considering accuracy, precision, F-measure and recall as metrics.

Download Full-text

Electricity Optimization in a Community Using OpenCV and Machine Learning with Mobile Application

Webology ◽

10.14704/web/v17i2/web17078 ◽

2020 ◽

Vol 17 (2) ◽

pp. 937-944

Author(s):

Arun Kumar Porchelvan ◽

S. Ganesh Kumar ◽

B. Muruganantham ◽

A. Murugan

Keyword(s):

Machine Learning ◽

Mobile Application ◽

Common Factors ◽

External Factors ◽

The Other ◽

Age Group ◽

Energy Usage ◽

Electrical Fields ◽

Gender And Age ◽

The Common

Optimizing the power is one of the basic need in growing development of electronic and electrical fields. With development of various fields leads to the increase in the demand of power directly or indirectly. The common factors helps in reducing the usage of power consumptions are the time and seasonality factors which is analyzed based on data over a period. The other factors which may be taken in consideration are the number people in a region and their characteristics like gender and age group. We can improvise the AI model using the above external factors. Once the model predict it update the expected amount of electricity required in transformer and send mobile notification to the authorized person and the person can track the energy usage in mobile for analysis.

Download Full-text

Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study

Energies ◽

10.3390/en14185947 ◽

2021 ◽

Vol 14 (18) ◽

pp. 5947

Author(s):

William Mounter ◽

Chris Ogwumike ◽

Huda Dawood ◽

Nashwan Dawood

Keyword(s):

Machine Learning ◽

Building Energy ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Energy Usage ◽

Research Attention ◽

Learning Techniques ◽

Term Energy

Advances in metering technologies and emerging energy forecast strategies provide opportunities and challenges for predicting both short and long-term building energy usage. Machine learning is an important energy prediction technique, and is significantly gaining research attention. The use of different machine learning techniques based on a rolling-horizon framework can help to reduce the prediction error over time. Due to the significant increases in error beyond short-term energy forecasts, most reported energy forecasts based on statistical and machine learning techniques are within the range of one week. The aim of this study was to investigate how facility managers can improve the accuracy of their building’s long-term energy forecasts. This paper presents an extensive study of machine learning and data processing techniques and how they can more accurately predict within different forecast ranges. The Clarendon building of Teesside University was selected as a case study to demonstrate the prediction of overall energy usage with different machine learning techniques such as polynomial regression (PR), support vector regression (SVR) and artificial neural networks (ANNs). This study further examined how preprocessing training data for prediction models can impact the overall accuracy, such as via segmenting the training data by building modes (active and dormant), or by days of the week (weekdays and weekends). The results presented in this paper illustrate a significant reduction in the mean absolute percentage error (MAPE) for segmented building (weekday and weekend) energy usage prediction when compared to unsegmented monthly predictions. A reduction in MAPE of 5.27%, 11.45%, and 12.03% was achieved with PR, SVR and ANN, respectively.

Download Full-text

The Impact of Data Segmentation in Predicting Monthly Building Energy Use with Support Vector Regression

Springer Proceedings in Energy - Energy and Sustainable Futures ◽

10.1007/978-3-030-63916-7_9 ◽

2021 ◽

pp. 69-76

Author(s):

William Mounter ◽

Huda Dawood ◽

Nashwan Dawood

Keyword(s):

Machine Learning ◽

Support Vector Regression ◽

Energy Use ◽

Building Energy ◽

Machine Learning Techniques ◽

Support Vector ◽

Energy Usage ◽

Energy Prediction ◽

Learning Techniques

AbstractAdvances in metering technologies and machine learning methods provide both opportunities and challenges for predicting building energy usage in the both the short and long term. However, there are minimal studies on comparing machine learning techniques in predicting building energy usage on their rolling horizon, compared with comparisons based upon a singular forecast range. With the majority of forecasts ranges being within the range of one week, due to the significant increases in error beyond short term building energy prediction. The aim of this paper is to investigate how the accuracy of building energy predictions can be improved for long term predictions, in part of a larger study into which machine learning techniques predict more accuracy within different forecast ranges. In this case study the ‘Clarendon building’ of Teesside University was selected for use in using it’s BMS data (Building Management System) to predict the building’s overall energy usage with Support Vector Regression. Examining how altering what data is used to train the models, impacts their overall accuracy. Such as by segmenting the model by building modes (Active and dormant), or by days of the week (Weekdays and weekends). Of which it was observed that modelling building weekday and weekend energy usage, lead to a reduction of 11% MAPE on average compared with unsegmented predictions.

Download Full-text