Abstract: Forecasting electricity load is important for various purposes like planning, operation and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria namely, the Mean Absolute Error and Root Mean Square Error. The National Renewable Energy Laboratory (NREL) residential energy consumption data are used to train the models. The results of this study show that SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts we can improve the robustness of the models for 24 hour ahead electricity load forecasting.
Abstract: Wind farms (WFs) with high level of penetration are
being established in power systems worldwide more rapidly than
other renewable resources. The Independent System Operator (ISO),
as a policy maker, should propose appropriate places for WF
installation in order to maximize the benefits for the investors. There
is also a possibility of congestion relief using the new installation of
WFs which should be taken into account by the ISO when proposing
the locations for WF installation. In this context, efficient wind farm
(WF) placement method is proposed in order to reduce burdens on
congested lines. Since the wind speed is a random variable and load
forecasts also contain uncertainties, probabilistic approaches are used
for this type of study. AC probabilistic optimal power flow (P-OPF)
is formulated and solved using Monte Carlo Simulations (MCS). In
order to reduce computation time, point estimate methods (PEM) are
introduced as efficient alternative for time-demanding MCS.
Subsequently, WF optimal placement is determined using generation
shift distribution factors (GSDF) considering a new parameter
entitled, wind availability factor (WAF). In order to obtain more
realistic results, N-1 contingency analysis is employed to find the
optimal size of WF, by means of line outage distribution factors
(LODF). The IEEE 30-bus test system is used to show and compare
the accuracy of proposed methodology.
Abstract: This paper proposed a novel model for short term load
forecast (STLF) in the electricity market. The prior electricity
demand data are treated as time series. The model is composed of
several neural networks whose data are processed using a wavelet
technique. The model is created in the form of a simulation program
written with MATLAB. The load data are treated as time series data.
They are decomposed into several wavelet coefficient series using
the wavelet transform technique known as Non-decimated Wavelet
Transform (NWT). The reason for using this technique is the belief
in the possibility of extracting hidden patterns from the time series
data. The wavelet coefficient series are used to train the neural
networks (NNs) and used as the inputs to the NNs for electricity load
prediction. The Scale Conjugate Gradient (SCG) algorithm is used as
the learning algorithm for the NNs. To get the final forecast data, the
outputs from the NNs are recombined using the same wavelet
technique. The model was evaluated with the electricity load data of
Electronic Engineering Department in Mandalay Technological
University in Myanmar. The simulation results showed that the
model was capable of producing a reasonable forecasting accuracy in
STLF.