Metaheuristic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Metaheuristhic approach to the Holt-Winters optimal short term load forecast

Electricity short-term load forecast is very important for the operation of power systems. In this work a classical exponential smoothing model, the Holt-Winters with double seasonality was used to test for accurate predictions applied to the Portuguese demand time series. Some metaheuristic algorithms for the optimal selection of the smoothing parameters of the Holt-Winters forecast function were used and the results after testing in the time series showed little differences among methods, so the use of the simple local search algorithms is recommended as they are easier to implement.

Wind-CSP short-term coordination by Milp approach

This paper is on the maximization of total profit in a day-ahead market for a price-taker producer needing a short-term scheduling for wind power plants coordination with concentrated solar power plants, having thermal energy storage systems. The optimization approach proposed for the maximization of profit is a mixed-integer linear programming problem. The approach considers not only transmission grid constraints, but also technical operating constraints on both wind and concentrated solar power plants. Then, an improved short-term scheduling coordination is provided due to the more accurate modelling presented in this paper. Computer simulation results based on data for the Iberian wind and concentrated solar power plants illustrate the coordination benefits and show the effectiveness of the approach.

Serum trace elements in dysphagic gastrostomy candidates before endoscopic gastrostomy for long term enteral feeding

Background & aims - Patients who underwent endoscopic gastrostomy (PEG) present protein-energy malnutrition, but little is known about Trace Elements (TE), Zinc (Zn), Copper (Cu), Selenium (Se), Iron (Fe), Chromium (Cr). Our aim was the evaluation of serum TE in patients who underwent PEG and its relationship with serum proteins, BMI and nature of underlying disorder. Methods - A prospective observational study was performed collecting: patient's age, gender, underlying disorder, NRS-2002, BMI, serum albumin, transferrin and TE concentration. We used ferrozine colorimetric method for Fe; Inductively Coupled Plasma-Atomic Emission Spectroscopy for Zn/Cu; Furnace Atomic Absorption Spectroscopy for Se/Cr. The patients were divided into head and neck cancer (HNC) and neurological dysphagia (ND). Results - 146 patients (89 males), 21–95 years: HNC-56; ND-90. Low BMI in 78. Low values mostly for Zn (n = 122) and Fe (n = 69), but less for Se (n = 31), Cu (n = 16), Cr (n = 7); low albumin in 77, low transferrin in 94 and 66 with both proteins low. Significant differences between the groups of underlying disease only for Zn (t140.326 = −2,642, p < 0.01) and a correlation between proteins and TE respectively albumin and Zn (r = 0.197, p = 0.025), and albumin and Fe (r = 0.415, p = 0.000). Conclusions - When gastrostomy was performed, patients display low serum TE namely Zn, but also Fe, less striking regarding others TE. It was related with prolonged fasting, whatever the underlying disease. Low proteins were associated with low TE. Teams taking care of PEG-patients should use Zn supplementation and include other TE evaluation as part of the nutritional assessment of PEG candidates.

Short-term electricity prices forecasting in a competetive market by a hybrid Pso-Anfis approach

In this paper, a novel hybrid approach is proposed for electricity prices forecasting in a competitive market, considering a time horizon of 1 week. The proposed approach is based on the combination of particle swarm optimization and adaptive-network based fuzzy inference system. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications, to demonstrate its effectiveness regarding forecasting accuracy and computation time. Finally, conclusions are duly drawn.