A regime-dependent artificial neural network technique for short-range solar irradiance forecasting

Abstract

Solar power can provide substantial power supply to the grid; however, it is also a highly variable energy source due to changes in weather conditions, i.e. clouds, that can cause rapid changes in solar power output. Independent systems operators (ISOs) and regional transmission organizations (RTOs) monitor the demand load and direct power generation from utilities, define operating limits and create contingency plans to balance the load with the available power generation resources. ISOs, RTOs, and utilities will require solar irradiance forecasts to effectively and efficiently balance the energy grid as the penetration of solar power increases. This study presents a cloud regime-dependent short-range solar irradiance forecasting system to provide 15-min average clearness index forecasts for 15-min, 60-min, 120-min and 180-min lead-times. A k-means algorithm identifies the cloud regime based on surface weather observations and irradiance observations. Then, Artificial Neural Networks (ANNs) are trained to predict the clearness index. This regime-dependent system makes a more accurate deterministic forecast than a global ANN or clearness index persistence and produces more accurate predictions of expected irradiance variability than assuming climatological average variability.

The future of forecasting for renewable energy

“…Typical methods include time-series models (auto-regression, and variants to incorporate spatial dependence) as well as machine learning approaches such as SVM and neural networks. Recent research in this area has focused on scaling-up these methodologies to be able to incorporate many spatial locations (Cavalcante, Bessa, Reis, & Browell, 2016;Messner & Pinson, 2018), and conditioning statistical model on large scale weather regimes for wind energy applications (Browell, Drew, & Philippopoulos, 2018) or cloud regimes for solar (McCandless, Haupt, & Young, 2016). Augmenting power production data with remote sensing is a well-established strategy for improving solar power forecast performance via incorporation of satellite imagery (Blanc, Remund, & Vallance, 2017) for hours-ahead forecasting and sky cameras (Chow et al, 2011;Kazantzidis et al, 2017) for intrahour forecasting.…”

Assessing Evidence for Weather Regimes Governing Solar Power Generation in Kuwait

“…Clouds present the greatest source of subhourly variability in GHI, with the fractional changes greatest at midday [2,17]. Furthermore, daily and seasonal changes in the zenith angle can introduce other errors in GHI measurements [38].…”

“…A thorough review of the common forecast approaches at different time aggregations is provided by [4], finding that almost 75% of methods are statistics based. Of these, deterministic irradiance forecasts often combine clustering analyses to classify the sky state [12][13][14][15], followed by a machine-based learning algorithm to develop forecasts [16][17][18][19]. However, clustering analyses are subjective and can result in model overfitting [20].…”

Short-Range Forecasting for Energy

“…Blended techniques, such as that of Pedro and Coimbra (2012), that use a genetic algorithm to optimize an ANN have also been effective. In addition, new methods blend weather observations with irradiance observations and use clustering techniques to identify regimes and then train ANN for the individual regimes (Kazor and Hering 2015), which was shown to improve upon non-regime-dependent ANN and upon smart persistence (McCandless et al 2016a). Recent work has added satellite data to this type of forecasting (McCandless et al 2016b).…”