Transforming Power Systems with AI Forecasting and Intelligent Sensing Technologies

Author: Ir. Dr. GOH Hui Hwang | 30 October, 2025

Introduction

With the transformation of the world energy system into a more sustainable one, the architecture of the modern electricity system is being reshaped by the connection of renewable energy sources (RES) and advanced digital technologies. Two of the most recent comprehensive reviews, one on the intelligent forecast for renewable energy generation and the other on the intelligent and advanced sensing technologies for new-type power systems, highlighted the need for the seamless integration of Artificial Intelligence (AI), machine learning (ML), and edge computing to provide a smarter, resilient, and adaptive energy infrastructure.

Intelligent Forecasting for Renewable Energy Generation

The research offers a comprehensive review of forecasting technologies relevant to renewable energy forecasting – namely solar and wind power. The variability and uncertainty of renewable energy sources necessitate multi-dimensional data processing and intelligent forecasting models according to the progressive perspectives and considerations of the study.

Forecasting approaches are classified in the study into deterministic and probabilistic models. Deterministic models offer single-point predictions using physical laws, statistical techniques, or artificial intelligence techniques. On the other hand, interval forecasts and probability distributions as output of probabilistic models are pivotal for risk-aware grid operation.

The forecasting is observed on ultra short (0-4 hours), short (4-24 hours) and medium-to-long-term (days to months) windows. There are three areas that require models – unit level, plant level, and multi-plant clusters – and these areas have different data and modeling needs spatially.

It addresses missing data, noise, and anomalies present in renewable energy datasets. And then, it lists all kinds of anomalies, for instance, peripheral-scattered, bottom-stacked, and so on and detects by traditional methods and AI-based methods such as Local Outlier Factor (LOF), K-means clustering, and Support Vector Machine (SVM).

The key point for good forecasting is to get predictive features from atmospheric, equipment, and environmental data. These include dimensionality reduction techniques like PCA, transfer learning, and generative adversarial networks (GANs) to maximize the input data we feed to the model.

It includes hybrid models which merge signal decomposition (e.g., CEEMDAN, VMD) and deep learning models (e.g., LSTM, CNN, and Transformer variants). These models exhibit better capabilities to accommodate and obtain nonlinear spatiotemporal relations dominance.

Conclusions

This complementary nature of the two studies illustrates a convergent trajectory for power system innovation (i.e., intelligent forecasting and advanced sensing technology reinforcing each other on the way toward adaptive, resilient, and efficient energy systems). Forecasting models driven by AI facilitate accurate energy planning, whilst smart sensors & corner cases of smart meters & renewable energy tracking have created the situational intelligence needed for either real-time or near-real-time management of the grid.

But the convergence of these technologies is more than technical upgrade. It is a transformation into autonomous, data-rich power systems that can journey through all the uncertainties of renewables and dynamic grid circumstances. Such innovations are likely to be at the heart of developing sustainable, reliable, and cost-effective power networks as the energy transition accelerates.

Ir. Dr. GOH Hui Hwang
Founder
IPM Group