This paper examines the motivation, applications and development of short-term solar forecasting using ground-based sky imagery for controlling equipment on electrical grids. Historically, there has not been a great deal of interaction between the fields of solar forecasting and electrical grid research. This situation is changing rapidly as solar forecasting is becoming increasingly important for dealing with the mass uptake of photovoltaics (PV) and solar-thermal generation on electrical grids around the world. The interactions in these two fields is examined, along with the opportunities for applying solar forecasting for on-grid and mini-grid applications. We review solar forecasting techniques, summarise their applications and evaluate the links to suitable techniques with applications. A review of current solar forecasting techniques, including Numerical Weather Prediction (NWP), statistical/data-driven approaches, and satellite techniques is presented. We also compare the characteristics of each technique to illustrate the requirements, status and suitability of each for use in short-term solar forecasting applications. The application of sky-camera ('skycam') forecasting in electrical grids is discussed in addition to the presentation of a detailed case study demonstrating the use of these techniques to enhance grid operation. The control strategy developed demonstrates an increase the real-time penetration of renewable power by dynamically adapting solar inverter setpoints according to cloud forecast data. A novel short-term solar forecasting system is presented which makes use of inexpensive ground-based sky imaging cameras (or 'skycams'). This system is able to predict changes in irradiance by forecasting cloud movement up to 20 min ahead, with a 10 s update frequency. Several novel techniques for skycam setup and forecasting are presented. These include: (a) A new, high-performance approach to cloud classification using a novel set of neural network input features. (b) A new method for calibrating a lens distortion model. (c) A novel technique using per-pixel cloud movement vectors to predict the timing and extent of sun shading events. This latter technique is capable of correctly classifying 97% of cloud pixels from a validation database of over 500,000 examples. Finally, we present a new technique for taking features extracted from sky-camera pixel data and building a model for predicting large shading events. We show an example of how this model can be easily adapted for either conservative or aggressive operation of a solar power system with a backup generator. In conservative operation, this model is shown to supply adequate or excess energy up to 99.96% of the time using 4 min-ahead forecasts when used for scheduling a backup electrical generator; meaning the system would require only minimal battery storage, while producing a large reduction in fossil-fuel consumption. Crown Copyright (C) 2014 Published by Elsevier Ltd. All rights reserved.