The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …

With the boom in Internet techniques and computer science, a variety of big data have been
introduced into forecasting research, bringing new knowledge and improving prediction …

This paper proposes a new local energy market design for distribution systems, which
integrates peer-to-peer (P2P) energy trading and probabilistic locational marginal pricing …

Residential load forecasting is a challenging problem due to complex relations among the
hourly electrical load values along the time and also nonlinear relationships among the …

This paper proposes a direct model for conditional probability density forecasting of
residential loads, based on a deep mixture network. Probabilistic residential load forecasting …

The main purpose of this paper is to develop an efficient machine learning model to estimate
the electric power load. The developed machine learning model can be used by electric …

With the development of data-driven models, extracting information from historical data for
better energy forecasting is critically important for energy planning and optimization in …

The reliable and an economic operation of the power system rely on an accurate prediction
of short term load. In this paper, a deep learning based Long Short Term Memory (LSTM) …

With the continuous development of smart grids, short-term power load forecasting has
become increasingly important in the operation of power markets and demand-side …

Electricity, a fundamental commodity, must be generated as per required utilization which
cannot be stored at large scales. The production cost heavily depends upon the source such …