A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization
Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review
Energy consumption models play an integral part in energy management and conservation,
as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying …
as it pertains to buildings. It can assist in evaluating building energy efficiency, in carrying …
[HTML][HTML] Data-driven modeling for long-term electricity price forecasting
Estimating the financial viability of renewable energy investments requires the availability of
long-term, finely-resolved electricity prices over the investment lifespan. This entails …
long-term, finely-resolved electricity prices over the investment lifespan. This entails …
Electricity spot prices forecasting based on ensemble learning
Efficient modeling and forecasting of electricity prices are essential in today's competitive
electricity markets. However, price forecasting is not easy due to the specific features of the …
electricity markets. However, price forecasting is not easy due to the specific features of the …
Probabilistic mid-and long-term electricity price forecasting
The liberalization of electricity markets and the development of renewable energy sources
has led to new challenges for decision makers. These challenges are accompanied by an …
has led to new challenges for decision makers. These challenges are accompanied by an …
A weighted LS-SVM based learning system for time series forecasting
TT Chen, SJ Lee - Information Sciences, 2015 - Elsevier
Time series forecasting is important because it can often provide the foundation for decision
making in a large variety of fields. Statistical approaches have been extensively adopted for …
making in a large variety of fields. Statistical approaches have been extensively adopted for …
Forecasting one-day-ahead electricity prices for italian electricity market using parametric and nonparametric approaches
Over the last three decades, accurate modeling and forecasting of electricity prices has
become a key issue in competitive electricity markets. As electricity price series usually …
become a key issue in competitive electricity markets. As electricity price series usually …
Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach
X Yan, NA Chowdhury - International Journal of Electrical Power & Energy …, 2013 - Elsevier
A hybrid mid-term electricity market clearing price (MCP) forecasting model combining both
least squares support vector machine (LSSVM) and auto-regressive moving average with …
least squares support vector machine (LSSVM) and auto-regressive moving average with …
Mid-term electricity market clearing price forecasting: A multiple SVM approach
X Yan, NA Chowdhury - International Journal of Electrical Power & Energy …, 2014 - Elsevier
In a deregulated electric market, offering the appropriate amount of electricity at the right
time with the right bidding price is of paramount importance for utility companies maximizing …
time with the right bidding price is of paramount importance for utility companies maximizing …