This work proposes a quantile regression neural network based on a novel constrained weighted quantile loss (CWQLoss) and its application to probabilistic short and medium-term electric-load forecasting of special interest for smart grids operations. The method allows any point forecast neural network based on a multivariate multi-output regression model to be expanded to become a quantile regression model. CWQLoss extends the pinball loss to more than one quantile by creating a weighted average for all predictions in the forecast window and across all quantiles. The pinball loss for each quantile is evaluated separately. The proposed method imposes additional constraints on the quantile values and their associated weights. It is shown that these restrictions are important to have a stable and efficient model. Quantile weights are learned end-to-end by gradient descent along with the network weights. The proposed model achieves two objectives: (a) produce probabilistic (quantile and interval) forecasts with an associated probability for the predicted target values. (b) generate point forecasts by adopting the forecast for the median (0.5 quantiles). We provide specific metrics for point and probabilistic forecasts to evaluate the results considering both objectives. A comprehensive comparison is performed between a selection of classic and advanced forecasting models with the proposed quantile forecasting model. We consider different scenarios for the duration of the forecast window (1 h, 1-day, 1-week, and 1-month), with the proposed model achieving the best results in almost all scenarios. Additionally, we show that the proposed method obtains the best results when an additive ensemble neural network is used as the base model. The experimental results are drawn from real loads of a medium-sized city in Spain.

Additive Ensemble Neural Network with Constrained Weighted Quantile Loss for Probabilistic Electric-Load Forecasting.

• Data mining and machine learning framework offer intelligent decision-making solutions from complex aquaculture and fisheries datasets. • Aquaculture applications such as monitoring and control of production environment, fish biomass and optimization of feed use are discussed with examples. • Fisheries management applications include surveillance of fishing, catch composition and ecosystem-fisheries associations. • Applications related to environment monitoring, fish processing and marketing are also indicated, along with challenges and perspectives. Aquaculture and fisheries sectors are finding ingenious ways to grow and meet the soaring human demand for nutrient-rich fish and seafood by efficiently utilizing the vast water resources and biodiversity of aquatic life on earth. This includes the progressive integration of information technology, data science and artificial intelligence with fishing and fish farming methods to enable intensification of aquaculture production, sustainable exploitation of natural fishery resources and mechanization-automation of allied activities. Exclusive data mining and machine learning systems are being developed to process complex datasets and perform intelligent tasks like analysing cause-effect associations, forecasting problems and providing smart-precision solutions for farming and catching fish. Considering the intensifying research and growing interest of stakeholders, in this review, we have consolidated basic information on the various practical applications of data mining and machine learning in aquaculture and fisheries domains from representative selection of scientific literature. This includes an overview of research and applications in (1) aquaculture activities such as monitoring and control of the production environment, optimization of feed use, fish biomass monitoring and disease prevention; (2) fisheries management aspects such as resource assessment, fishing, catch monitoring and regulation; (3) environment monitoring related to hydrology, primary production and aquatic pollution; (4) automation of fish processing and quality assurance systems; and (5) fish market intelligence, price forecasting and socioeconomics. While aquaculture has been relatively faster in integrating data mining and machine learning tools with advanced farming systems, capture fisheries is finding reliable methods to sort the complexities in data collection and processing. Finally, we have pointed out some of the challenges and future perspectives related to large-scale adoption. 

Applications of data mining and machine learning framework in aquaculture and fisheries: A review

As the companies have grown larger, the salmon farming industry has received increased attention from investors, financial analysts and other representatives of the financial community. Still, litt...

Modeling the return distribution of salmon farming companies: A quantile regression approach

Salmon price is highly volatile and hard to predict, which obscures planning decisions and raises financing costs for market participants. This study considers hedging the spot price uncertainty wi...

Hedging salmon price risk

Forecasting crude oil prices with shrinkage methods: Can nonconvex penalty and Huber loss help?

This study establishes a benchmark for short-term salmon price forecasting. The weekly spot price of Norwegian farmed Atlantic salmon is predicted 1–5 weeks ahead using data from 2007 to 2014. Sixteen alternative forecasting methods are considered, ranging from classical time series models to customized machine learning techniques to salmon futures prices. The best predictions are delivered by k-nearest neighbors method for 1 week ahead; vector error correction model estimated using elastic net regularization for 2 and 3 weeks ahead; and futures prices for 4 and 5 weeks ahead. While the nominal gains in forecast accuracy over a naive benchmark are small, the economic value of the forecasts is considerable. Using a simple trading strategy for timing the sales based on price forecasts could increase the net profit of a salmon farmer by around 7%.

Daumantas Bloznelis

Papers

Short‐term salmon price forecasting

Hedging salmon price risk

Composite versus model-averaged quantile regression