Energy Usage Dataset - Revision history

Admino739mjm7: /* Resources */

2024-08-19T06:40:15Z

Resources

← Older revision		Revision as of 06:40, 19 August 2024
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	== Resources ==		== Resources ==
	<ul>		<ul>
	<li>~~''Note: Input~~ data, output data and data model ~~will be~~ available ~~soon~~ in [https://github.com Github]''</li>		<li>The Energy Usage Dataset (input data, output data and data model)is available in [https://github.com/AI-REDGIO-5-0/Energy-Usage-Dataset/tree/main Github]</li>
	<li>Created by [https://www.pbn.hu/main.php?Lang=EN/ PBN] / [https://www.am-lab.hu/main.php?Lang=EN/ am-LAB Digital Innovation Hub]</li>		<li>Created by [https://www.pbn.hu/main.php?Lang=EN/ PBN] / [https://www.am-lab.hu/main.php?Lang=EN/ am-LAB Digital Innovation Hub]</li>
	<li>Contact gyula.gal@pbn.hu / martin.dan@pbn.hu</li>		<li>Contact gyula.gal@pbn.hu / martin.dan@pbn.hu</li>

Admino739mjm7: /* Usage */

2024-07-18T14:02:57Z

Usage

← Older revision		Revision as of 14:02, 18 July 2024
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	* Required Python libraries such as scikit-learn and pandas installed.		* Required Python libraries such as scikit-learn and pandas installed.
	** You can install them with pip: ''pip install scikit-learn pandas''		** You can install them with pip: ''pip install scikit-learn pandas''
	~~</p>~~


	=== Loading the Asset ===		=== Loading the Asset ===

Admino739mjm7: /* Usage */

2024-07-18T14:02:39Z

Usage

← Older revision		Revision as of 14:02, 18 July 2024
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	** You can install them with pip: ''pip install scikit-learn pandas''		** You can install them with pip: ''pip install scikit-learn pandas''
	</p>		</p>
	~~<br>~~

	~~<p style="line-height: 1.5em">~~

	=== Loading the Asset ===		=== Loading the Asset ===
			<p style="line-height: 1.5em">
	* Download/Access Dataset: Ensure you have access to the dataset file (Output_data.csv).		* Download/Access Dataset: Ensure you have access to the dataset file (Output_data.csv).
	* Load the Data: Use pandas to load the CSV file into a DataFrame:		* Load the Data: Use pandas to load the CSV file into a DataFrame:
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	=== Licence===		=== Licence===
	Open source		Open source

	== Resources ==		== Resources ==

Admino739mjm7: /* Resources */

2024-07-12T15:11:59Z

Resources

← Older revision		Revision as of 15:11, 12 July 2024
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	== Resources ==		== Resources ==
	<ul>		<ul>
	<li>Input data, output data and data model available in [https://github.com Github]</li>		<li>''Note: Input data, output data and data model will be available soon in [https://github.com Github]''</li>
	<li>Created by [https://www.pbn.hu/main.php?Lang=EN/ PBN] / [https://www.am-lab.hu/main.php?Lang=EN/ am-LAB Digital Innovation Hub]</li>		<li>Created by [https://www.pbn.hu/main.php?Lang=EN/ PBN] / [https://www.am-lab.hu/main.php?Lang=EN/ am-LAB Digital Innovation Hub]</li>
	<li>Contact gyula.gal@pbn.hu / martin.dan@pbn.hu</li>		<li>Contact gyula.gal@pbn.hu / martin.dan@pbn.hu</li>

Admino739mjm7: /* Model Usage */

2024-07-12T15:10:21Z

Model Usage

← Older revision		Revision as of 15:10, 12 July 2024
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	** ''model = joblib.load('model.joblib')''		** ''model = joblib.load('model.joblib')''
	* Prepare the Data: Make sure the dataset columns match the model's expected input format. This might involve feature engineering or scaling		* Prepare the Data: Make sure the dataset columns match the model's expected input format. This might involve feature engineering or scaling
	input_features = data [ [		input_features = data
			[ [
	'rootNodeIsRun_value',		'rootNodeIsRun_value',
	'upcoming_watt_hours',		'upcoming_watt_hours',
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	'internal_dc_input_voltage' ] ]		'internal_dc_input_voltage' ] ]
	* Make Predictions: Use the loaded model to make predictions on new data		* Make Predictions: Use the loaded model to make predictions on new data
	** predictions = model.predict(input_features)		** ''predictions = model.predict(input_features)''

	=== Licence===		=== Licence===

Admino739mjm7: /* Model Usage */

2024-07-12T15:09:52Z

Model Usage

← Older revision		Revision as of 15:09, 12 July 2024
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	=== Model Usage ===		=== Model Usage ===
	* Loading the Model: Ensure that the model file (model.joblib) is in the same directory or provide the correct path. Load the model using joblib:		* Loading the Model: Ensure that the model file (model.joblib) is in the same directory or provide the correct path. Load the model using joblib:
	** import joblib		** ''import joblib''
	** model = joblib.load('model.joblib')		** ''model = joblib.load('model.joblib')''
	* Prepare the Data: Make sure the dataset columns match the model's expected input format. This might involve feature engineering or scaling		* Prepare the Data: Make sure the dataset columns match the model's expected input format. This might involve feature engineering or scaling
	input_features = data[[		input_features = data [ [
	'rootNodeIsRun_value',		'rootNodeIsRun_value',
	'upcoming_watt_hours',		'upcoming_watt_hours',

Admino739mjm7: /* Loading the Asset */

2024-07-12T15:09:11Z

Loading the Asset

← Older revision		Revision as of 15:09, 12 July 2024
Line 76:		Line 76:
	* Download/Access Dataset: Ensure you have access to the dataset file (Output_data.csv).		* Download/Access Dataset: Ensure you have access to the dataset file (Output_data.csv).
	* Load the Data: Use pandas to load the CSV file into a DataFrame:		* Load the Data: Use pandas to load the CSV file into a DataFrame:
	** import pandas as pd		** ''import pandas as pd''
	** data = pd.read_csv('Output_data.csv')		** ''data = pd.read_csv('Output_data.csv')''

	=== Model Usage ===		=== Model Usage ===

Admino739mjm7 at 15:08, 12 July 2024

2024-07-12T15:08:37Z

← Older revision		Revision as of 15:08, 12 July 2024
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	The predictive model is a Decision Tree Classifier built using scikit-learn, a popular machine learning library in Python. The model was trained to predict outcomes based on the dataset's features, focusing on optimizing decisions related to energy management.		The predictive model is a Decision Tree Classifier built using scikit-learn, a popular machine learning library in Python. The model was trained to predict outcomes based on the dataset's features, focusing on optimizing decisions related to energy management.
	</p>		</p>
	~~<br>~~


	== Usage ==		== Usage ==
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	Before using this asset, ensure you have:		Before using this asset, ensure you have:
	* Python installed on your system (recommended version 3.9 or newer)		* Python installed on your system (recommended version 3.9 or newer)
	* Required Python libraries such as scikit-learn and pandas installed. You can install them with pip: pip install scikit-learn pandas		* Required Python libraries such as scikit-learn and pandas installed.
			** You can install them with pip: ''pip install scikit-learn pandas''
	</p>		</p>
	<br>		<br>
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	** data = pd.read_csv('Output_data.csv')		** data = pd.read_csv('Output_data.csv')

			=== Model Usage ===
	~~The asset~~ is ~~Available~~ ''~~(Note~~: ~~For demonstration purposes, we use~~ the ~~casting product image~~ data ~~for quality inspection dataset available at~~ [~~https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product Kaggle]. However~~, ~~a similar logic could be applied to other industries and product lines.)~~''		* Loading the Model: Ensure that the model file (model.joblib) is in the same directory or provide the correct path. Load the model using joblib:
	~~</p>~~		** import joblib
			** model = joblib.load('model.joblib')
	~~=== Walkthroughs===~~		* Prepare the Data: Make sure the dataset columns match the model's expected input format. This might involve feature engineering or scaling
	* ~~Instructions available [https~~:~~//github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML?tab=readme-ov-file#instructions here]</li>~~		input_features = data[[
	* ~~Detailed step-by-step demontration of the Quality Control asset can be found [https://github~~.~~com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML/blob/main/Quality%20control%20on%20production%20lines%20with%20TinyML_v2.pdf here]</li>~~		'rootNodeIsRun_value',
			'upcoming_watt_hours',
			'dailyWattage_watt_hours',
			'remaining_watt_hours',
			'dc_input_power',
			'ac_input_power',
			'ac_output_power',
			'dc_output_power',
			'power_generation',
			'total_battery_percent',
			'ac_output_on',
			'dc_output_on',
			'ac_input_voltage',
			'internal_dc_input_voltage' ] ]
			* Make Predictions: Use the loaded model to make predictions on new data
			** predictions = model.predict(input_features)

	=== Licence===		=== Licence===
	~~MIT License, licence link [https://github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML/blob/main/LICENSE here]~~		Open source

	== Resources ==		== Resources ==
	<ul>		<ul>
	<li>~~Notebook~~ available in [https://github.com~~/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML~~ Github]</li>		<li>Input data, output data and data model available in [https://github.com Github]</li>
	<li>Created by [https://~~expertai-lux~~.~~com~~/ ~~ExpertAI~~-~~Lux S~~.~~à r~~.l]</li>		<li>Created by [https://www.pbn.hu/main.php?Lang=EN/ PBN] / [https://www.am-lab.hu/main.php?Lang=EN/ am-LAB Digital Innovation Hub]</li>
	<li>Contact ~~george~~.~~fatouros~~@~~expertai-lux~~.~~com~~ / ~~george~~.~~makridis~~@~~expertai-lux~~.~~com~~</li>		<li>Contact gyula.gal@pbn.hu / martin.dan@pbn.hu</li>
	</ul>		</ul>

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	==== Acknowledgement====		==== Acknowledgement====
	<p style="font-size:90%;line-height: 1.5em">		<p style="font-size:90%;line-height: 1.5em">
	''~~This work is an update of ExpertAI-Lux S.à r.l work in the context of the H2020 [https://www.airegio-project.eu/ AI REGIO project] (Grant Agreement No 952003). Further updates performed~~ in the ~~context~~ of the AI REDGIO 5.0 project~~, concern;~~ (1) ~~leveraging tensorflow lite with the aim of being able to deploy on an edge device, and (2) post-training quantization with the aim of making the model more resource~~-~~efficient~~.''		''The dataset was created in the framework of the AI REDGIO 5.0 project as DF XIII PBN am-LAB (HUNGARY): SUNSYNC: AI SOLUTION FOR OPTIMIZING RECYCLING IN INDUSTRY AT THE LEVEL OF AM-LAB’S DF.''
	</p>		</p>

	== Relevant Categories==		== Relevant Categories==
	[[Category:~~Jupyter Notebook~~]][[Category:~~Computer Vision~~]][[Category:~~Deep Learning~~]][[Category:~~Edge AI~~]][[Category:~~Quality Control~~]][[Category:~~Machine Vision~~]][[Category:Cloud-based]][[Category:Edge]]		[[Category:Model]][[Category:Dataset]][[Category:Machine_Learning]][[Category:Process_Optimisation]][[Category:Resource_Planning]][[Category:IoT]][[Category:Cloud-based]][[Category:Edge]]

Admino739mjm7: Created page with "Predictive model and dataset for energy management optimisation
Image 1: Snapshot from the SunSync Dataset
== Asset Description ==
This asset provides a predictive model and dataset aimed at optimising energy management processes. Built using a decision tree classifier and trained on historical energy usage data, the model f..."

2024-07-12T14:39:23Z

Created page with "Predictive model and dataset for energy management optimisation thumb|right|<div style="font-size:88%;line-height: 1.5em">Image 1: Snapshot from the SunSync Dataset</div> == Asset Description == This asset provides a predictive model and dataset aimed at optimising energy management processes. Built using a decision tree classifier and trained on historical energy usage data, the model f..."

New page

Predictive model and dataset for energy management optimisation [[File:SunSync Dataset Snip.png|thumb|right|<div style="font-size:88%;line-height: 1.5em">Image 1: Snapshot from the SunSync Dataset</div>]]

== Asset Description ==

This asset provides a predictive model and dataset aimed at optimising energy management processes. Built using a decision tree classifier and trained on historical energy usage data, the model forecasts upcoming power needs and identifies optimal decision states. This implementation helps organizations streamline their energy usage, improve resource planning, and enhance the efficiency of their energy systems, whether through preventive maintenance or better alignment of generation with consumption.

 

== Dataset and Model Details ==
=== Dataset Overview ===

The dataset comprises 3,466 entries each represented by 17 features that capture a variety of system metrics and states related to energy usage. These features include measurements of power (input and output), wattage predictions, battery status, and operational flags. The data format is primarily numerical with timestamps, facilitating time series analysis and predictive modelling.

 

Key attributes include:
{| class="wikitable" style="margin:auto; width:100%; line-height: 1.5em"
|-
! Feature !! Description
|-
| Id|| A unique identifier for a specific device or data entry in the system.
|-
| Timestamp|| A record of the date and time when an event, measurement, or data entry took place.
|-
| rootNodeIsRun_value|| A status flag indicating whether the recycling module (energy consumer) is currently running.
|-
| Upcoming Watt Hours|| A forecast of the amount of energy (in watt-hours) expected to be generated in the upcoming hour.
|-
| Daily Wattage (Watt Hours)|| The total amount of energy predicted to be generated in a day, measured in watt-hours.
|-
| Remaining Watt Hours|| An indication of the amount of energy (in watt-hours) left to produce for the remainder of the day.
|-
| DC Input Power|| The power (in watts) entering the system as Direct Current (DC), typically from solar panels or other DC sources.
|-
| AC Input Power|| The power (in watts) entering the system as Alternating Current (AC), potentially from the solar panel or other AC sources.
|-
| AC Output Power|| The power (in watts) being supplied by the system in AC form, to power devices.
|-
| DC Output Power|| The power (in watts) being supplied by the system in DC form, possibly for charging batteries or powering DC devices.
|-
| Power Generation || The total power (in watts) being generated by solar panels at the moment.
|-
| Total Battery Percent || The current charge level of the battery system, expressed as a percentage of its total capacity.
|-
| AC Output On || A status flag indicating whether the AC output of the system is currently active or turned on.
|-
| DC Output On || A similar status flag indicating whether the DC output of the system is active or turned on.
|-
| AC Input Voltage || The voltage level (in volts) of the AC power entering the system.
|-
| Internal DC Input Voltage || The voltage level (in volts) of the internal DC power within the system.
|-
| decision || A binary target variable representing a decision state.
|}

=== Model Details ===

The predictive model is a Decision Tree Classifier built using scikit-learn, a popular machine learning library in Python. The model was trained to predict outcomes based on the dataset's features, focusing on optimizing decisions related to energy management.

 

== Usage ==
=== Prerequisites ===

Before using this asset, ensure you have:
* Python installed on your system (recommended version 3.9 or newer)
* Required Python libraries such as scikit-learn and pandas installed. You can install them with pip: pip install scikit-learn pandas

 



=== Loading the Asset ===
* Download/Access Dataset: Ensure you have access to the dataset file (Output_data.csv).
* Load the Data: Use pandas to load the CSV file into a DataFrame:
** import pandas as pd
** data = pd.read_csv('Output_data.csv')

The asset is Available ''(Note: For demonstration purposes, we use the casting product image data for quality inspection dataset available at [https://www.kaggle.com/datasets/ravirajsinh45/real-life-industrial-dataset-of-casting-product Kaggle]. However, a similar logic could be applied to other industries and product lines.)''


=== Walkthroughs===
* Instructions available [https://github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML?tab=readme-ov-file#instructions here]</li>
* Detailed step-by-step demontration of the Quality Control asset can be found [https://github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML/blob/main/Quality%20control%20on%20production%20lines%20with%20TinyML_v2.pdf here]</li>

=== Licence===
MIT License, licence link [https://github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML/blob/main/LICENSE here]

== Resources ==
<ul>
<li>Notebook available in [https://github.com/giorgosfatouros/Quality-Control-in-Industry-with-CV-and-TinyML Github]</li>
<li>Created by [https://expertai-lux.com/ ExpertAI-Lux S.à r.l]</li>
<li>Contact george.fatouros@expertai-lux.com / george.makridis@expertai-lux.com</li>
</ul>

==== Acknowledgement====

''This work is an update of ExpertAI-Lux S.à r.l work in the context of the H2020 [https://www.airegio-project.eu/ AI REGIO project] (Grant Agreement No 952003). Further updates performed in the context of the AI REDGIO 5.0 project, concern; (1) leveraging tensorflow lite with the aim of being able to deploy on an edge device, and (2) post-training quantization with the aim of making the model more resource-efficient.''


== Relevant Categories==
[[Category:Jupyter Notebook]][[Category:Computer Vision]][[Category:Deep Learning]][[Category:Edge AI]][[Category:Quality Control]][[Category:Machine Vision]][[Category:Cloud-based]][[Category:Edge]]