[HTML][HTML] Interpretable machine learning for building energy management: A state-of-the-art review
Abstract Machine learning has been widely adopted for improving building energy efficiency
and flexibility in the past decade owing to the ever-increasing availability of massive building …
and flexibility in the past decade owing to the ever-increasing availability of massive building …
Review of low voltage load forecasting: Methods, applications, and recommendations
The increased digitalisation and monitoring of the energy system opens up numerous
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
opportunities to decarbonise the energy system. Applications on low voltage, local networks …
Deep-learning-based short-term electricity load forecasting: A real case application
The rising popularity of deep learning can largely be attributed to the big data phenomenon,
the surge in the development of new and novel deep neural network architectures, and the …
the surge in the development of new and novel deep neural network architectures, and the …
An interval decomposition-ensemble approach with data-characteristic-driven reconstruction for short-term load forecasting
Short-term load forecasting is crucial for power demand-side management and the planning
of the power system. Considering the necessity of interval-valued time series modeling and …
of the power system. Considering the necessity of interval-valued time series modeling and …
[HTML][HTML] Short-term electricity load forecasting—A systematic approach from system level to secondary substations
Energy forecasting covers a wide range of prediction problems in the utility industry, such as
forecasting demand, generation, price, and power load over time horizons and different …
forecasting demand, generation, price, and power load over time horizons and different …
[HTML][HTML] Comprehensive review of load forecasting with emphasis on intelligent computing approaches
In this paper, a comprehensive review is presented for mid-term load forecasting. The basic
loads and effective factors are studied, and then several classifications are presented for …
loads and effective factors are studied, and then several classifications are presented for …
Short-term load forecasting of electricity demand for the residential sector based on modelling techniques: a systematic review
In this paper, a systematic literature review is presented, through a survey of the main digital
databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly …
databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly …
[HTML][HTML] Intrinsically interpretable machine learning-based building energy load prediction method with high accuracy and strong interpretability
Black-box models have demonstrated remarkable accuracy in forecasting building energy
loads. However, they usually lack interpretability and do not incorporate domain knowledge …
loads. However, they usually lack interpretability and do not incorporate domain knowledge …
Daily peak electrical load forecasting with a multi-resolution approach
In the context of smart grids and load balancing, daily peak load forecasting has become a
critical activity for stakeholders in the energy industry. An understanding of peak magnitude …
critical activity for stakeholders in the energy industry. An understanding of peak magnitude …
[HTML][HTML] Examining the drivers of the imbalance price: Insights from the balancing mechanism in the United Kingdom
The increasing integration of renewable energy sources in the UK electricity sector has
posed challenges to the stability of the system, leading to a sharp rise in the costs of …
posed challenges to the stability of the system, leading to a sharp rise in the costs of …