Abstract
Short-Term Load Forecasting plays a significant role in energy generation planning, and is specially gaining momentum in the emerging Smart Grids environment, which usually presents highly disaggregated scenarios where detailed real-time information is available thanks to Communications and Information Technologies, as it happens for example in the case of microgrids. This paper presents a two stage prediction model based on an Artificial Neural Network in order to allow Short-Term Load Forecasting of the following day in microgrid environment, which first estimates peak and valley values of the demand curve of the day to be forecasted. Those, together with other variables, will make the second stage, forecast of the entire demand curve, more precise than a direct, single-stage forecast. The whole architecture of the model will be presented and the results compared with recent work on the same set of data, and on the same location, obtaining a Mean Absolute Percentage Error of 1.62% against the original 2.47% of the single stage model.
Highlights
The term Smart Grid (SG) is associated with the new concept of “smart” electricity distribution networks, whose aim is the introduction of intelligence for the optimization of the production and distribution of electricity.In trying to meet the electricity demand with sufficient energy, utilities need to anticipate this demand by using estimate forecasts, usually 24 h ahead, and be able to know if they will need to buy energy in the market, or sell it
This paper presents a model based on Artificial Neural Network (ANN) which makes STFL of the day in a microgrid environment, using variables such as input to the network and estimated values of the day intended to be forecast
Mean Absolute Percentage Error (MAPE) obtained for Peak Load Forecasting (PLF) and VLF are optimal if they are compared with the results of [5], which MAPE varies between
Summary
In trying to meet the electricity demand with sufficient energy, utilities need to anticipate this demand by using estimate forecasts, usually 24 h ahead, and be able to know if they will need to buy energy in the market (energy defect), or sell it (excess energy). As shown in [3], SG enables the bi-directional flow of electric energy and information between utilities and consumers It facilitates the integration within the network of the increasingly popular renewable generation sources, by promoting the participation of end users in energy saving and cooperating with the DR mechanism. The main objective of DR is the reduction of peak load within its control environment, whether it is a distribution network, a Smart City (SC) or a microgrid
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