Is the first textbook on load forecasting for the distribution network. Brings together both statistical and machine learning topics. Includes colorful illustrations and practical examples from many sectors and developing countries. Is open access, which means that you have free and unlimited access.

The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 3639 smart meter customers, our density predictions for 24h-ahead load forecasting compare favorably against Gaussian and Gaussian mixture densities. Furthermore, they outperform a non-parametric approach based on the pinball loss, especially in low-data scenarios..

This paper presents a literature review on the topic of Low Voltage (LV) load forecasting. It gives an overview of the approaches, core applications, datasets, trends, and challenges. Suggestions how to facilitate the continued improvement and advancement are given and a set of recommendations toward best practices are provided.

A building’s structural mass does provide inherent thermal storage capabilities. Within this work, a mathematical model of a building inertia thermal energy storage (BITES) is proposed to allow integration into optimized smart grid control for real-world applications.