In Competitive electricity markets, Electricity price forecasting is very valuable for all participants. Good forecasting plays a very important role in power investment decision and transmission expansion.
- High volatility
- High frequency
- Nonlinearity
- Mean-reversion
- Non-stationarity
- Calendar-effect
- Price spikes
● Extreme learning machines are feedforward neural networks for
classification, regression and clustering with a single layer or
multiple layers of hidden nodes, where the parameters of hidden
nodes need not be tuned.
● These hidden nodes can be randomly assigned and never
updated.
● The output weights of hidden nodes are usually learned in a
single step, which essentially amounts to learning a linear
model.
● PSO was first intended for simulating social behaviour, as a
stylized representation of the movement of organisms in a
bird flock or fish school.
● PSO is a metaheuristic as it makes few or no assumptions
about the problem being optimized and can search very large
spaces of candidate solutions
- Initially we have set of Candidate solutions (called Particles).
- These particles are moved around in the search-space
- Every Particle have three direction to move
3.1. Its current direction
3.2. Particle’s best direction
3.3. Swarm’s best direction
Time Series is a set of observations on the
values that a variable takes at different time.
Ex.Sales trend, Stock market prices, weather
forecasts etc.
1.Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods - ZhangYangabLiCeaLiLiana https://doi.org/10.1016/j.apenergy.2016.12.130