Our #EmissionsWatch delivers systematic analysis and long-term forecasts of RGGI, CSAPR, SO₂, NOₓ, and CO₂ markets while tracking federal, regional, and state regulatory developments that shape emissions pricing and projections.

Learn more: buff.ly/fPkPUgl

#EnergyForecasting #CarbonRegulation

Europe’s power grid & #weather patterns are changing quickly. See how decision grade weather data helps #utilities manage volatility and support trading and grid stability in episode 3 of our European utilities webinar series: dtn.link/9wwozn

#EnergyForecasting #UtilityIndustry #Webinar #Replay

Europe’s power grid & #weather patterns are changing quickly. See how decision grade weather data helps #utilities manage volatility and support trading and grid stability in episode 3 of our European utilities webinar series: dtn.link/9wwozn

#EnergyForecasting #UtilityIndustry #Webinar #Replay

Utilities across Europe rely on precise forecasting. Join us on 3 December for Ep. 3 to see how Decision-Grade Data supports confident planning & #GridStability. LinkedIn Live: dtn.link/4x3jbr
Zoom: dtn.link/0jgafs

#Utilities #EnergyForecasting #WeatherIntelligence #GridResilience

Texas's pragmatic all-of-the-above energy strategy proves successful despite calls for reliance on single resources Texas's unique energy strategy, which balances multiple resources like natural gas, wind, solar, and nuclear to maintain grid stability, is proving effective as the state prepares for significant growth in power demand. A new analysis projects Texas will account for over 30% of the nation's electrical growth by 2030, driven in part by data centers, and will handle roughly half of the US's industrial electricity demand growth during this period. However, despite this diversification, grid reliability remains a concern, with NERC warning that Texas is at an "elevated" risk of outages this winter, highlighting ongoing challenges as the state expands its energy infrastructure.

Texas's pragmatic all-of-the-above energy strategy proves successful despite calls for reliance on single resources #ERCOT #EnergyDiversity #GridResilience #SolarPower #EnergyForecasting #EnergyReliability

Episode 3 of our European #utilities #webinar series is coming on 3 December. DTN experts will share how better forecasting & decisioning tools strengthen trading & #GridResilience.
Join on LinkedIn: dtn.link/4x3jbr
Or RSVP on Zoom: dtn.link/0jgafs

#Utilities #EnergyForecasting #WeatherIntelligence

Foundation Models Match Transformers for Household Power Forecasting

Foundation models can predict short‑term household electricity demand as accurately as custom‑trained transformers, and Chronos and TimesFM surpass transformers when using larger input windows. https://arxiv.org/abs/2410.09487 #foundationmodels #energyforecasting

How modernized energy forecasting can make grid operations more efficient An ESIG report highlights why traditional approaches to long-term load and DER forecasting need to evolve to support the modern grid.

Energy forecasting must evolve as renewables, DERs & extreme weather reshape the grid. ESIG urges scenario-based models to boost reliability & smarter planning.

Learn more: www.renewableenergyworld.com/power-grid/s...

#EnergyForecasting #SmartGrid #DER #UtilityPlanning

News from E+E Leader 📊 Campus energy forecasting just got a major AI upgrade.

Researchers at @mizzou slashed forecasting errors by 46% using machine learning tools like XGBoost—paving the way for… http://dlvr.it/TMF253 #EnergyForecasting #MachineLearning #Sustainability #AIInnovation #DataScience

Utah utility discusses heating degree days and rate forecasting methods Utility representatives evaluate data collection methods and their impact on customer billing rates.

Utah's utility leaders are proposing a dramatic shift to a three-year rate review cycle, aiming to stabilize your energy bills amidst fluctuating usage trends.

Click to read more!

#UT #CitizenPortal #RateStability #DataTransparency #EnergyForecasting

A snapshot from DeepVision shows heat indices (in ºF) for the US on Monday afternoon.

We provide targeted forecasts with the lead time to help utilities prepare, protect assets, and prevent outages: spire.com/weather-clim...

#Utilities #EnergyForecasting #GridReliability #SevereWeather #LoadForecasting

Incorporating Artificial Intelligence for Precise Energy Forecasting - Cozzy Energy Solutions Incorporating Artificial Intelligence for Precise Energy Forecasting ================================================================= As the world continues to rely heavily on electricity, accurate forecasting of hourly electricity demand is crucial for efficient energy management. Researchers have been working tirelessly to develop advanced algorithms that can predict energy consumption with high accuracy. In a recent study published in 2025, El-Azab et al. explored the use of machine and deep learning algorithms to forecast hourly electricity demand. The researchers aimed to identify the key factors that influence energy consumption and evaluate the performance of different algorithms in predicting these factors. The Key Factors Influencing Energy Consumption --------------------------------------------- The study identified three primary factors that impact hourly electricity demand: temperature, day-type (e.g., weekday vs. weekend), and electricity price. Temperature plays a significant role in determining energy consumption patterns, with extreme temperatures often leading to increased heating or cooling demands. Day-type also affects energy consumption, as people tend to consume more energy during weekdays due to work and other activities. Machine Learning Algorithms: A Traditional Approach ------------------------------------------------- The researchers employed traditional machine learning algorithms, including gated recurrent units (GRUs), long short-term memory (LSTM) networks, and adaptive neuro-fuzzy inference system (ANFIS). While these algorithms showed promise, they were found to be less accurate than their deep learning counterparts. Deep Learning Algorithms: The Game-Changer ------------------------------------------ The study revealed that deep learning algorithms outperformed traditional machine learning methods in predicting hourly electricity demand. Specifically, the LSTM network achieved the lowest mean absolute percentage error (MAPE) values for temperature and day-type factors. This suggests that incorporating advanced machine and deep learning algorithms can significantly improve the accuracy of energy forecasting. Policy Implications ----------------- The findings of this study have significant implications for energy policy and management decisions. By leveraging advanced machine and deep learning algorithms, power system operators can optimize energy production and distribution, manage peak demand periods more effectively, and mitigate the impact of extreme weather events on power systems. In conclusion, incorporating artificial intelligence into energy forecasting is crucial for making informed decisions about energy supply and demand management. The study by El-Azab et al. highlights the potential benefits of using advanced machine and deep learning algorithms to improve the accuracy of hourly electricity demand forecasting.

Incorporating Artificial Intelligence for Precise Energy Forecasting #ISONE #EnergyForecasting #ArtificialIntelligence #MachineLearning #DeepLearning #ElectricityDemand