A review of machine learning in building load prediction
The surge of machine learning and increasing data accessibility in buildings provide great
opportunities for applying machine learning to building energy system modeling and …
opportunities for applying machine learning to building energy system modeling and …
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches
Background: With the development of smart grids, accurate electric load forecasting has
become increasingly important as it can help power companies in better load scheduling …
become increasingly important as it can help power companies in better load scheduling …
Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm
Nowadays, a basic commodity for a human being to lead a standard lifestyle with human
comfort irrespective of the nature of environmental conditions is electric power. The …
comfort irrespective of the nature of environmental conditions is electric power. The …
A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets
In recent years, clean energies, such as wind power have been developed rapidly.
Especially, wind power generation becomes a significant source of energy in some power …
Especially, wind power generation becomes a significant source of energy in some power …
Big data driven smart energy management: From big data to big insights
K Zhou, C Fu, S Yang - Renewable and sustainable energy reviews, 2016 - Elsevier
Large amounts of data are increasingly accumulated in the energy sector with the
continuous application of sensors, wireless transmission, network communication, and cloud …
continuous application of sensors, wireless transmission, network communication, and cloud …
Deep neural network based demand side short term load forecasting
In the smart grid, one of the most important research areas is load forecasting; it spans from
traditional time series analyses to recent machine learning approaches and mostly focuses …
traditional time series analyses to recent machine learning approaches and mostly focuses …
A review on applications of ANN and SVM for building electrical energy consumption forecasting
The rapid development of human population, buildings and technology application currently
has caused electric consumption to grow rapidly. Therefore, efficient energy management …
has caused electric consumption to grow rapidly. Therefore, efficient energy management …
Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia
Accurate and reliable forecasting models for electricity demand (G) are critical in
engineering applications. They assist renewable and conventional energy engineers …
engineering applications. They assist renewable and conventional energy engineers …
A new feature selection technique for load and price forecast of electrical power systems
Load and price forecasts are necessary for optimal operation planning in competitive
electricity markets. However, most of the load and price forecast methods suffer from lack of …
electricity markets. However, most of the load and price forecast methods suffer from lack of …