How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead

Kazmi, Hussain; Tao, Zhenmin

doi:10.1016/j.apenergy.2022.119565

Cited by 19 publications

(6 citation statements)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Depending on the size and availability of local storage, this may or may not be acceptable. This despite the fact that the model accuracy seems to be in line with, or even better than, the overall Swedish national load forecast produced by Svenska kraftnät (Kazmi & Tao, 2022). Forecasting national load is arguably an easier task due to its long history and considerably more in-house knowledge.…”

Section: The Road Ahead and Future Usagementioning

confidence: 94%

Applied Machine Learning for Short-Term Electric Load Forecasting in Cities - A Case Study of Eskilstuna, Sweden

Netzell,

Kazmi,

Kyprianidis

2023

Linköping Electronic Conference Proceedings

View full text Add to dashboard Cite

With the growing demand, electrification, and renewable proliferation, the necessity of being able to forecast future demand in combination with flexible energy usage is tangible. Distribution network operators often have a power capacity limit agreed with the regional grid, and economic penalties await if crossed. This paper investigates how cities could deal with these issues using data-driven approaches. Hierarchical electric load data is analyzed and modeled using Multiple Linear Regression. Key calendar variables holidays, industry vacation, ”Hour of day” and ”Day of week” are identified alongside the meteorological heating-, and cooling degree hours, global irradiance, and wind speed. This inexpensive algorithm outperforms the benchmark ”weekly Naïve” with a relative Root Mean Squared Error of 35% for the year-long rolling origin evaluation. Learnings from the data exploration and modeling are then used to evaluate the AI-based model Light Gradient Boosting Machine. Using similar explanatory variables for this expensive algorithm results in a relative error of 45%, although it outperforms the previous one during the summer. The models have varying strengths and weaknesses and could advantageously be combined into an ensemble model for improving accuracy. Incorporating detailed knowledge of local renewable electricity production in combination with hierarchical forecasting could further increase accuracy. With domain knowledge and statistical analysis, it is possible to create robust load forecasts with acceptable accuracy using easily available machine-learning libraries. Both models have good potential to be used as input to economic optimization and load shifting.

show abstract

Section: The Road Ahead and Future Usagementioning

confidence: 94%

Applied Machine Learning for Short-Term Electric Load Forecasting in Cities - A Case Study of Eskilstuna, Sweden

Netzell,

Kazmi,

Kyprianidis

2023

Linköping Electronic Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…This level of accuracy may or may not be acceptable, depending on the limit and the size and availability of the electrical energy storage or other flexible energy assets. Despite achieving an accuracy comparable to or better than the overall Swedish national load forecast produced by Svenska kraftnät [40], internal discussions with the local grid operator of Eskilstuna indicate the need for improved precision, especially in anticipation of future energy storage investments.…”

Section: Navigating Future Challengesmentioning

confidence: 99%

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

Netzell,

Kazmi,

Kyprianidis

2024

Energies

Self Cite

View full text Add to dashboard Cite

As the demand for electricity, electrification, and renewable energy rises, accurate forecasting and flexible energy management become imperative. Distribution network operators face capacity limits set by regional grids, risking economic penalties if exceeded. This study examined data-driven approaches of load forecasting to address these challenges on a city scale through a use case study of Eskilstuna, Sweden. Multiple Linear Regression was used to model electric load data, identifying key calendar and meteorological variables through a rolling origin validation process, using three years of historical data. Despite its low cost, Multiple Linear Regression outperforms the more expensive non-linear Light Gradient Boosting Machine, and both outperform the “weekly Naïve” benchmark with a relative Root Mean Square Errors of 32–34% and 39–40%, respectively. Best-practice hyperparameter settings were derived, and they emphasize frequent re-training, maximizing the training data size, and setting a lag size larger than or equal to the forecast horizon for improved accuracy. Combining both models into an ensemble could the enhance accuracy. This paper demonstrates that robust load forecasts can be achieved by leveraging domain knowledge and statistical analysis, utilizing readily available machine learning libraries. The methodology for achieving this is presented within the paper. These models have the potential for economic optimization and load-shifting strategies, offering valuable insights into sustainable energy management.

show abstract

“…However, small-scale PV installations are generally invisible to transmission system operators (TSOs), meaning their generated power is not monitored 3 . For TSOs, the lack of reliable rooftop PV measurements increases the flexibility needs, i.e., the means for the grid to compensate for load or supply variability 4 – 7 . Estimating the PV power generation from meteorological data is common practice to overcome the lack of power measurements.…”

Section: Background and Summarymentioning

confidence: 99%

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Kasmi

Saint‐Drenan

Trebosc³

et al. 2023

Sci Data

View full text Add to dashboard Cite

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale, rooftop PV installations are deployed at an unprecedented pace, and their safe integration into the grid requires up-to-date, high-quality information. Overhead imagery is increasingly being used to improve the knowledge of rooftop PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be reliably transferred from one region or imagery source to another without incurring a decrease in accuracy. To address this issue, known as distribution shift, and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, segmentation masks, and installation metadata (i.e., technical characteristics). We provide installation metadata for more than 28000 installations. We supply ground truth segmentation masks for 13000 installations, including 7000 with annotations for two different image providers. Finally, we provide installation metadata that matches the annotation for more than 8000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.

show abstract

How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead

Cited by 19 publications

References 41 publications

Applied Machine Learning for Short-Term Electric Load Forecasting in Cities - A Case Study of Eskilstuna, Sweden

Applied Machine Learning for Short-Term Electric Load Forecasting in Cities - A Case Study of Eskilstuna, Sweden

Deriving Input Variables through Applied Machine Learning for Short-Term Electric Load Forecasting in Eskilstuna, Sweden

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

Contact Info

Product

Resources

About