Power (Management) - Revision history

BPeat at 15:56, 13 September 2023

2023-09-13T15:56:55Z

BPeat at 00:07, 17 August 2023

2023-08-17T00:07:21Z

BPeat at 23:30, 9 July 2023

2023-07-09T23:30:03Z

BPeat at 20:34, 3 July 2023

2023-07-03T20:34:27Z

BPeat at 15:38, 12 December 2021

2021-12-12T15:38:55Z

BPeat at 15:36, 12 December 2021

2021-12-12T15:36:39Z

BPeat at 11:38, 22 September 2020

2020-09-22T11:38:55Z

BPeat at 12:17, 31 August 2020

2020-08-31T12:17:04Z

BPeat at 12:15, 31 August 2020

2020-08-31T12:15:19Z

BPeat at 12:06, 31 August 2020

2020-08-31T12:06:38Z

@@ Line 22: / Line 22: @@
 ** [[Chemistry]]
 * [[Energy]] Use with AI Algorithms
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [http://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | Richard Evans & Jim Gao]
 * [http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42542.pdf Machine Learning Applications for Data Center Optimization | Jim Gao, Google]

@@ Line 22: / Line 22: @@
 ** [[Chemistry]]
 * [[Energy]] Use with AI Algorithms
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 * [http://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | Richard Evans & Jim Gao]
 * [http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42542.pdf Machine Learning Applications for Data Center Optimization | Jim Gao, Google]

@@ Line 22: / Line 22: @@
 ** [[Chemistry]]
 * [[Energy]] Use with AI Algorithms
-* [[Anomaly Detection]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 * [http://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | Richard Evans & Jim Gao]
 * [http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42542.pdf Machine Learning Applications for Data Center Optimization | Jim Gao, Google]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=power+consumption+management+efficient+artificial+intelligence Youtube search...]

@@ Line 12: / Line 12: @@
 ** [[Resources & Utilities]]
 ** [[Chemistry]]
-** [[Energy]] Use with AI Algorithms
+* [[Energy]] Use with AI Algorithms
 * [[Anomaly Detection]]
 * [http://deepmind.com/blog/deepmind-ai-reduces-google-data-centre-cooling-bill-40/ DeepMind AI Reduces Google Data Centre Cooling Bill by 40% | Richard Evans & Jim Gao]

@@ Line 45: / Line 45: @@
 {| class="wikitable" style="width: 550px;"
 ||
-<youtube>VzyzKv_LBRw</youtube>
+<youtube>rAC-BIxsL6w</youtube>
-<b>Saving Energy Consumption With Deep Learning
+<b>PyCon HK 2017 - Machine Learning on Energy Consumption Prediction
-</b><br>Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris.
+</b><br>PyCon Hong Kong 2017 Talk  Machine Learning on Energy Consumption Prediction - by Benny Huang The project is in HKU deep learning lab. We analyze a large set of energy consumption data and build real time application based on the prediction. The topic involves different machine learning approaches and comparison. Since the machine learning is often discussed, I will also talk about the often ignored skill in data science. How to handle huge data and optimize the processing code, how to effectively build useful tools and make the life of data scientist easier, and how to correctly interpret the predicting result to enhance the project level from merely "machine learning" to real "data-driven application"
 |}
 |}<!-- B -->
@@ Line 62: / Line 62: @@
 {| class="wikitable" style="width: 550px;"
 ||
-<youtube>rAC-BIxsL6w</youtube>
+<youtube>VzyzKv_LBRw</youtube>
-<b>PyCon HK 2017 - Machine Learning on Energy Consumption Prediction
+<b>Saving Energy Consumption With Deep Learning
-</b><br>PyCon Hong Kong 2017 Talk  Machine Learning on Energy Consumption Prediction - by Benny Huang The project is in HKU deep learning lab. We analyze a large set of energy consumption data and build real time application based on the prediction. The topic involves different machine learning approaches and comparison. Since the machine learning is often discussed, I will also talk about the often ignored skill in data science. How to handle huge data and optimize the processing code, how to effectively build useful tools and make the life of data scientist easier, and how to correctly interpret the predicting result to enhance the project level from merely "machine learning" to real "data-driven application"
+</b><br>Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris.
 |}
 |}<!-- B -->

@@ Line 21: / Line 21: @@
 ||
 <youtube>bgPm3mcrqAc</youtube>
-<b>HH1
+<b>Machine Learning Applications for Energy Efficiency and Customer Care
-</b><br>BB1
+</b><br>With the world’s largest residential energy dataset at our fingertips, Opower is uniquely situated to use Machine Learning to tackle problems in demand-side management. Our communication platform, which reaches millions of energy customers, allows us to build those solutions into our products and make a measurable impact on energy efficiency, customer satisfaction, and cost to utilities. In this talk, we will survey several Machine Learning projects that we’ve been working on. These projects vary from predicting customer propensity to clustering load curves for behavioral segmentation, and leverage supervised and unsupervised techniques.
 |}
 |<!-- M -->
@@ Line 29: / Line 29: @@
 ||
 <youtube>Pftge_ewxYQ</youtube>
-<b>HH2
+<b>DeepMind AI reduces energy used for cooling Google Data Centers by 40%
-</b><br>BB2
+</b><br>From smartphone assistants to image recognition and translation, machine learning already helps us in our everyday lives. But it can also help us to tackle some of the world’s most challenging physical problems -- such as energy consumption.  Large-scale commercial and industrial systems like data centers consume a lot of energy, and while much has been done to stem the growth of energy use, there remains a lot more to do given the world’s increasing need for computing power. Google is taking many steps to reduce energy consumptions. Compared to five years ago, Google  now get around 3.5 times the computing power out of the same amount of energy. By applying DeepMind’s machine learning to its own data centers, Google managed to reduce the amount of energy  it use for cooling by up to 40 percent. In any large scale energy-consuming environment, this would be a huge improvement. Given how sophisticated Google’s data centers are already, it’s a phenomenal step forward. The implications are significant for Google’s data centers, given its potential to greatly improve energy efficiency and reduce emissions overall. This will also help other companies who run on Google’s cloud to improve their own energy efficiency. Every improvement in data center efficiency reduces total emissions into our environment and with technology like DeepMind’s, we can use machine learning to consume less energy and help address one of the biggest challenges of all -- climate change.
 |}
 |}<!-- B -->
@@ Line 38: / Line 38: @@
 ||
 <youtube>zYcBAlueoNU</youtube>
-<b>HH3
+<b>Becoming a BrainBox AI Licensed Reseller Webinar
-</b><br>BB3
+</b><br>Learn more about becoming a BrainBox AI Licensed Reseller and how you can provide your real estate clients with up to 25% in energy savings in less than 3 months. Launched only a few months ago, BrainBox AI’s Licensed Reseller program is already comprised of over 20 organizations from across North America and abroad, and includes System Integrators, ESCOs and everything in between. This webinar aims to introduce BrainBox AI’s technology and to demonstrate the benefits of adding our AI solution to your portfolio. Join our information webinar, to learn more about: - BrainBox AI’s market leading AI-driven HVAC optimization technology - Benefits of becoming a BrainBox AI licensed reseller - Partnership structure and process
 |}
 |<!-- M -->
@@ Line 46: / Line 46: @@
 ||
 <youtube>VzyzKv_LBRw</youtube>
-<b>HH4
+<b>Saving Energy Consumption With Deep Learning
-</b><br>BB4
+</b><br>Discover how big data, GPUs, and deep learning, can enable smarter decisions on making your building more energy-efficient with AI startup, Verdigris.
 |}
 |}<!-- B -->
@@ Line 55: / Line 55: @@
 ||
 <youtube>SYURbwV2-5I</youtube>
-<b>HH5
+<b>Energy Demand Forecasting with MACHINE LEARNING
-</b><br>BB5
+</b><br>Machine Learning to predict on-campus energy use, Develop a Multiple Regression Model, Include weekend variable (Binary), Compare the Mean Absolute Percent Error (MAPE), Multiple Regression vs. Neural Network Model
 |}
 |<!-- M -->
@@ Line 63: / Line 63: @@
 ||
 <youtube>rAC-BIxsL6w</youtube>
-<b>HH6
+<b>PyCon HK 2017 - Machine Learning on Energy Consumption Prediction
-</b><br>BB6
+</b><br>PyCon Hong Kong 2017 Talk  Machine Learning on Energy Consumption Prediction - by Benny Huang The project is in HKU deep learning lab. We analyze a large set of energy consumption data and build real time application based on the prediction. The topic involves different machine learning approaches and comparison. Since the machine learning is often discussed, I will also talk about the often ignored skill in data science. How to handle huge data and optimize the processing code, how to effectively build useful tools and make the life of data scientist easier, and how to correctly interpret the predicting result to enhance the project level from merely "machine learning" to real "data-driven application"
 |}
 |}<!-- B -->
@@ Line 72: / Line 72: @@
 ||
 <youtube>iodUmvgKgbY</youtube>
-<b>HH1
+<b>Wattics Energy Management Analytics: Machine Learning Forecasting
-</b><br>BB1
+</b><br>Don’t waste any more time analysing raw measurements. Let our analytics engines compare your energy use against expected patterns and alert you when your consumption becomes abnormal. Here is a brief tutorial of the Sentinel Trends tab on our Wattics dashboard. If you have any questions, please visit our website at www.wattics.com or contact us at support@wattics.com.
 |}
 |<!-- M -->
@@ Line 80: / Line 80: @@
 ||
 <youtube>kYWw6GBRjVk</youtube>
-<b>HH2
+<b>Internet of Things Optimization
-</b><br>BB2
+</b><br>The Internet of things lives! More and more devices are coming prepackaged with internet access that wouldn’t normally be. That includes everything from smart salt shakers to smart tabletops. And because these devices have a connection to the world wide web, they can communicate with the outside world and each other, sharing information and even learning from one another. In this video, I'll explain how to use a reinforcement learning technique called "Monte Carlo" to optimize electricity consumption and cooling demands for a smart home. Enjoy!
 |}
 |}<!-- B -->